Detecting outliers and learning complex structures with large spectroscopic surveys – a case study with APOGEE stars

Detecting outliers and learning complex structures with large spectroscopic surveys – a case... Abstract In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data. methods: data analysis, stars: general, stars: peculiar 1 INTRODUCTION Extracting and analysing information from ongoing and future astronomical surveys, with their increasing size and complexity, requires astronomers to take advantage of the tools developed in the (also rapidly growing) fields of data science and machine learning (ML). Most commonly in astronomy, these methods enable detection or classification of specified objects using supervised ML algorithms, while unsupervised ML is used to search for correlations or clusters in high dimensional data. Recent examples for such work are Bloom et al. (2012) – identification and classification of transits and variable stars using imaging, Meusinger et al. (2012) – outlier detection with quasar spectra, Masci et al. (2014) – classification of periodic variable stars using photometric time series, Baron et al. (2015) – clustering diffuse interstellar band lines based on their pairwise correlation, Miller et al. (2017) – Star-Galaxy classification based on imaging. A review of data science applications in astronomy can be found in Ball & Brunner (2010). In this work, we focus on unsupervised exploration of a data set based on a similarity matrix, containing a pair-wise similarity measure between every two objects (the simplest possible measure being the Euclidean distance between the features of two objects). We show that such a similarity matrix (or its inverse, the distance matrix) is a powerful tool for exploring a data set in a data-driven way. We calculate an unsupervised random forest (RF) based similarity measure for stellar spectra and show that, without any additional input other than the spectra themselves, the similarity matrix traces physical properties such as metallicity, effective temperature and surface gravity. This allows us to visualize the complex structure of the data set, to query for similar objects based on their spectra alone, to put an object in the context of the general population, to scan the data set for different object types, and to detect outliers. These possibilities are only partly available with traditional representation of an object in an astronomical data base, i.e. by its fit parameters. Fitting a model requires making assumptions about the object. This can work well for a large fraction of the data, but usually cannot account for all the objects nor all of the features. In data sets composed of astronomical spectra, the model fitting is usually based on spectral templates, that do not cover the entire range of parameters available in the data set. Furthermore, templates are usually not available for rare or unexpected objects. This leaves a fraction of the objects, even if well understood, not well fitted, and impossible to query using the data base. Generative models have recently gained popularity within the astronomical community and outside of it, as they solve some of the issues raised above. Generative models are generated from the data set, with few to no assumption about the data structure and distribution of information content. These models, which are purely data-driven, have been shown to generalize well, and describe even the most extreme objects in the sample without the need for dedicated treatment. An example of generative models is generative adversarial neural networks (GANs), for a recent use in astrophysics see Schawinski et al. (2017). In this work, we show that the unsupervised RF algorithm can be viewed as a generative model, as it grasps complex features in the data set, and is able to describe the most extreme objects in the sample in the same context as the common ones. Perhaps the most intriguing usage of a similarity matrix is outlier detection. Outliers in a data set can have different origins and interpretations. Some are measurement or data processing errors, and others are objects not expected to be in the data set, extreme and rare objects, and most importantly, unknown unknowns – objects we did not know we should be looking for. In addition, in astronomy, rare objects could actually be important and common evolutionary phases that are short lived, and therefore challenging to observe. It is worth noting that finding the mundane outliers is still useful in order to clean the data set from erroneous and unwanted objects, to allow for a better analysis of the rest of the sample. Outlier detection algorithms can be divided into different types: (i) Distance based algorithms, which we use in this work, relying on a (case specific) definition of a pair-wise distance between the objects, (ii) Probabilistic algorithms, based on estimating the probability density function of the data, (iii) Domain based algorithms, which create boundaries in feature space, (iv) Reconstruction based algorithms, which model the data and calculate the reconstruction error as a measure for novelty, and (v) Information-theoretic algorithms, which use the information content of the data (for example by computing the entropy of the data), and measure how specific objects in the data set change this value. For a review, see Pimentel et al. (2014). We use a distance based algorithm as it allows us to explore the data in additional ways, as discussed above. One thing to note is that for a large and complex enough data set it is likely that there is not a single outlier detection algorithm that is best, i.e. one algorithm that detects all the interesting outliers. In general, different algorithms could be sensitive to different types of outliers. An obvious test for such an algorithm is whether it detects the expected outliers, if it does then it could be worthwhile to investigate all the detected outliers. But even then there is no guarantee that a different algorithm would not detect additional interesting objects. In this work, we expand the outlier detection algorithm presented in Baron & Poznanski (2017)1 and apply it to infrared stellar spectra. The core of the algorithm is calculating a distance matrix of the objects in the sample. This distance is based on RF dissimilarity. For RF see Breiman et al. (1984); Breiman (2001), for RF dissimilarity see Breiman & Cutler (2003); Shi & Horvath (2006). There are many possible choices for a similarity measure, a simple example being the euclidian distance between the features of the objects. See Yang (2006) for a survey of distance metric learning. It is known [see Yang (2006), and references therein] that a good choice of distance metric can improve the accuracy of K-nearest-neighbour classification (a common application of a distance metric), over simple euclidian distances. Similarly to outlier detection algorithms, there is no best distance metric, even for a specific data set. As there are many possible usages for a distance metric, it is even less clear how such best distance metric would be defined. An intuitive reason to use RF dissimilarity is that, as described below, it is sensitive to the correlation between different features. This is is often of importance in spectra. For instance, line ratios are usually of more interest then the strength of a single line. A euclidian distance metric will be more sensitive to strength of single lines. See Garcia-Dias et al. (2018) for an application of an euclidian distance metric in a clustering algorithm with APOGEE spectra. Baron & Poznanski (2017) applied this algorithm to find outliers in galaxy spectra from Sloan Digital Sky Survey (SDSS; Eisenstein et al. 2011) and used the distance matrix to detect outliers. They found spectra showing various rare phenomena such as supernovae, galaxy–galaxy gravitational lenses, and double peaked emission-lines, as well as the first reported evidence for active galactic nucleus (AGN)-driven outflows, traced by ionized gas, in post starburst E+A galaxies. The last discovery is discussed in Baron et al. (2017). The algorithm was applied to galaxy spectra using the flux values at every wavelength as features for the RF (i.e. without generating user defined features). Here, we do the same with stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE, Majewski, APOGEE Team & APOGEE-2 Team 2016), which is part of the SDSS-III, and explore additional applications of the distance matrix produced by the algorithm. We visualize the distance matrix using the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm (van der Maaten & Hinton 2008), find objects which are similar to objects of interest, and find the most similar objects in the data set (that is – spectroscopic twins). This paper is organized as follows. Section 2 describes the APOGEE data set we use in this work. In Section 3, we use t-SNE to visualize the distance matrix produced by our algorithm, and show that it traces stellar parameters. We use the distance matrix to find groups of similar objects, and spectroscopic twins. In Section 4, we discuss ways to select and classify outliers efficiently. In Section 5, we present the classification of the outliers we detected. We summarize in Section 6. 2 APOGEE SPECTRA The 14th SDSS data release (DR14; Abolfathi et al. 2017) contains the first data release for the APOGEE-2 survey. The APOGEE-2 survey consists of high resolution (R ∼ 22 500), high signal to noise ratio (S/N; typically S/N > 100), infrared H-band (1.51–1.70 μm) spectra for ∼263 000 different stars. The APOGEE-2 main survey spans all galactic environments (bulge, disc, and halo) and is composed mainly of red giant stars. The main survey targets were chosen using a cut on the H-band magnitude, gravity-sensitive optical photometry, and dereddened (J − Ks)0 colour limits. The colour limit and optical photometry criteria are intended to separate red giants from main sequence dwarfs. The APOGEE-2 data set contains ∼32 000 non-main survey targets, including ∼13 300 ancillary targets, and ∼27 000 hot stars used for telluric correction. More details on the target selection in APOGEE-2 are in Zasowski et al. (2017). A large fraction of the work done with APOGEE data is devoted to investigating the Milky Way structure and evolution using chemical abundances and radial velocities (RVs) derived from the spectra; for examples, see Frinchaboy et al. (2013); Nidever et al. (2014); Bovy et al. (2014); Ness et al. (2015); Chiappini et al. (2015); Hayden et al. (2015). We note that APOGEE spectra are rich with information, and a single spectrum can contain hundreds of absorption lines. The input to our algorithm is the pseudo-continuum normalized (PCN) spectrum. The PCN procedure is done with the APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP; García Pérez et al. 2016) in order to remove variations of spectral shape arising from interstellar reddening, errors in relative fluxing, detector response, and broad-band atmospheric absorption. The APOGEE spectra contain two gaps in wavelength. Our preprocessing stage consists of removing flux values in these gaps (these values are set to zero in the original PCN spectrum), as well as interpolating the spectra to the same wavelength grid. This leaves us with 7514 flux values per object, which are the features used by the outlier detection algorithm. Applying our algorithm to the APOGEE-2 spectra from DR14, it became clear that many objects have faulty PCN spectra (these objects are discussed in Section 5). Our algorithm naturally classifies these objects as outliers, making it harder to find the more interesting outliers. For this reason, since DR13 does not suffer from this contamination, we apply the algorithm to DR13 data as well. DR13 contains spectra for 163 000 stars, 25 000 of which are non-main survey. DR13 contains results from APOGEE-1, for which the target selection is somewhat different, and is described in Zasowski et al. (2013). Unless otherwise stated the results presented in this paper refer to DR14, APOGEE-2 (which we will refer to as APOGEE) data. We use only objects with S/N > 100, of which there are 193 556 in DR14 (107 390 in DR13). The input data size is therefore the product of the number of objects by the number of features (wavelengths). The reason for not using low S/N objects is that when included, objects with spectra dominated by noise are detected as outliers. We note that for the high S/N objects we used, the weirdness score and the S/N are not correlated. 3 EXPLORING THE APOGEE DATA SET USING A DISTANCE MATRIX Using our distance matrix to find physically interesting outliers and study the structure of the data set requires it to retain the complex information that we see in each object in the sample, which is a non-trivial task. In this section, we explore what type of information our distance matrix contains. Baron & Poznanski (2017) have seen some hints that the RF distance matrix contains a wealth of complex spectral information aggregated to a single number, the pair-wise distance, here to explore that question using visualization and dimensionality reduction tools. 3.1 Random forest dissimilarity Briefly, the distance is calculated by the following procedure. First, synthetic data are created with the same marginal distributions as the original data in every feature, but stripped of the correlation between different features (the features in our application are the flux values at each wavelength of the spectra, as described below). Having two types of objects, one real and one synthetic, an RF classifier is trained to separate between the two. In the process of separating the synthetic objects with un-correlated features from the real ones, the RF learns to recognize correlations in the spectra of real objects. The RF is composed of a large number of classification trees, each tree is trained to separate real and synthetic objects using a subset of the data (the ‘Random’ in’RF’ is referring to the randomness in which a subset of the data is selected for each tree, see Breiman et al. (1984); Breiman (2001) for details). Having a large number of trees, the similarity S between two objects (objects in the original data set, i.e. real objects) is then calculated by counting the number of trees in which the two objects ended up on the same leaf (a leaf being a tree node with no children nodes), and dividing by the number of trees. This is done only for the trees in which both objects are classified as real. We define the distance matrix to be D = 1 − S. Using the distance matrix, we can calculate a ‘weirdness score’ for every object, defined to be the average distance to all other objects. Below we refer to this weirdness score as Wall. See Baron & Poznanski (2017) for a detailed description of the algorithm. To build the distance matrix, we use the scikit-learn implementation of RF. The number of trees we used is 5000. We note that this number was necessary to reach convergence, i.e. increasing this number further does not alter the results. Every 200 trees are built using a random subset of 10 000 objects. 3.2 The t-SNE algorithm t-SNE is a dimensionality reduction algorithm that is particularly well suited for the visualization of high-dimensional data sets. We use t-SNE to visualize our distance matrix. A-priori, these distances could define a space with almost as many dimensions as objects, i.e. tens of thousand of dimensions. Obviously, since many stars are quite similar, and their spectra are defined by a few physical parameters, the minimal spanning space might be smaller. By using t-SNE, we can examine the structure of our sample projected into 2D. We use our distance matrix as input to the t-SNE algorithm and in return get a 2D map of the objects in our data set. In this map, nearby objects have a small pair-wise distance, and distant objects have a large pair-wise distance. The two t-SNE dimensions have no physical interpretation. Since the dimensionality in greatly reduced in the process, this is approximate, and breaks for large distances. That is, the map does not show the relative pair-wise distance between ‘far away’ and ‘very far away’ objects. The map does preserve small scale structure. The general idea of the t-SNE algorithm is quite simple – trying to preserve the distances of each object to its nearest neighbours (the number of which is determined by the perplexity parameter), while forcing the distances to reside on a lower dimensional plane, in our case 2D. There is usually no single best t-SNE map. Maps calculated with different numbers of nearest neighbours can provide the user with different information about the data set. For example, a map calculated with 10 000 nearest neighbours is not likely to show a cluster that contains 100 objects, while a map with 100 nearest neighbours is. Other free parameters in t-SNE are of computational nature, and control speed versus accuracy (accuracy of approximations done in different calculations inside the algorithm). A bad choice of parameters is usually manifested by a large fraction of the objects distributed randomly on the map. We consider a map in which all or almost all of the objects are located in structures to be a good map. Once we have that we can change the perplexity to determine the ‘scale’ in which the objects are clustered. A guide for effective use of t-SNE is available in Wattenberg, Viégas & Johnson (2016). We use the scikit-learn (Pedregosa et al. 2011) implementation of t-SNE. We note that to get informative maps we had to significantly increase the learning rate parameter (in the t-SNE map shown below it was set to 40 000) from its default value of 1000. The perplexity we used was 2000. Both of these parameters required adjustment when changing the number of objects in the distance matrix. Building the map took about 3 d of computation on a machine with 32 cores and 1TB of RAM. When using the current version of scikit-learn (0.17), t-SNE is using memory of about eight times the size of the distance matrix. The memory usage will be significantly reduced in future scikit-learn versions. We used the development version of t-SNE, which will be included in scikit-learn 0.19. With this version the memory usage was reduced by roughly a factor of 4, depending on the perplexity. 3.3 A t-SNE map of the APOGEE data set We apply the t-SNE algorithm to our RF dissimilarity distance matrix. The map produced can be especially informative when using different object attributes to colour the points. In Fig. 1, we use the following for colour: Teff, highlight of M-type stars, metallicity, and log(g), based on the ASPCAP fit. Most of the objects lie in a right-hand side, mainly vertical, component of the map. In this part, we see that the stars are sequenced by their surface gravity, where giants are located at the top and dwarfs at the bottom, as well as their effective temperature for which we get two separate sequences, one for dwarfs at the bottom of the map and one for giants at the top of the map. We also see an horizontal sequence that follows the metallicity, high metallicity on the right-hand side. On the left-hand side of the map, we have the hotter stars in the APOGEE sample, including the stars used for telluric calibration. The very low metallicity stars are located near these telluric objects, both having mainly featureless spectra. Figure 1. View largeDownload slide t-SNE map of our distance matrix. Each point on the map represents a star, where spectrally similar objects cluster on small scales. The axes do not have any physical significance. In the different panels, different coloring schemes are presented. Panel (a): effective temperature, panel (b): surface gravity, panel (c): metallicity, and panel (d): highlighted M-type stars. The values used for the different coloring are taken from ASPCAP. Stars with no available value for a parameter do not appear on the map. For example, many dwarf stars do not have log g values, so the clusters containing dwarfs disappear from the log g map. The complex structure of the sample is apparent. In panels (e) and (f), we colour the map by the weirdness score. Wall is in panel (e), and W250 is in panel (f). We see that when using W250, low Teff stars no longer dominate the high weirdness score population, and we get a more diverse outlier population that is spread on the t-SNE map. Figure 1. View largeDownload slide t-SNE map of our distance matrix. Each point on the map represents a star, where spectrally similar objects cluster on small scales. The axes do not have any physical significance. In the different panels, different coloring schemes are presented. Panel (a): effective temperature, panel (b): surface gravity, panel (c): metallicity, and panel (d): highlighted M-type stars. The values used for the different coloring are taken from ASPCAP. Stars with no available value for a parameter do not appear on the map. For example, many dwarf stars do not have log g values, so the clusters containing dwarfs disappear from the log g map. The complex structure of the sample is apparent. In panels (e) and (f), we colour the map by the weirdness score. Wall is in panel (e), and W250 is in panel (f). We see that when using W250, low Teff stars no longer dominate the high weirdness score population, and we get a more diverse outlier population that is spread on the t-SNE map. In panel 1d, we see that some M-type stars are located far from the rest. We manually inspect these objects as an example to see if this is due to the algorithm mis-locating a few objects, or if these objects are really different from the rest of their respective groups. We find that in this case the objects really have different looking spectra, with poor ASPCAP fitting. For example, some of these misplaced-M-type stars turn out to be B-type emission line stars (Be stars). From the t-SNE maps, we learn that our distance matrix is capable of aggregating non-trivial information about the objects in the sample. Fig. 1 shows that the distance matrix hold information about various physical properties, namely the figure is showing sequences in the effective temperature, surface gravity, and metallicity. These properties, in addition to the chemical abundances, affect the spectral features in non-trivial and partly degenerate ways, which we see are captured in the distance matrix. The APOGEE pipeline derives the stellar parameters by means of best-fitting templates. We see that some of the stars in the sample do not have derived parameter values. This is usually because these stars are extreme, at least with respect to the rest of the sample, and their stellar parameters fall outside the grid of spectral templates used by the pipeline. One can use the distance matrix to find these objects. As seen on the t-SNE map, the algorithm places the extreme objects next to the less extreme ones in a continuous sequence. In this sense, we say that the similarity measure could be viewed as a generative model of the data. Seeing here that globally the distance matrix captures the structure of the data set, in the next two subsections we see that it could also be used to investigate the data set at a ‘smaller scale’ – by looking at the most similar objects. 3.4 Object retrieval We can use the distance matrix to query the data set for similar objects based on their spectra alone. We use the example of carbon rich stars to show that the algorithm can find objects that were not possible to find using their ASPCAP fit parameters. Carbon-rich stars have atmospheres with over-abundance of carbon compared to oxygen. In this case, the excess carbon (i.e. carbon that is not tied in CO) will allow CN (and other carbon molecules) to form. In regular stars, there will be excess oxygen, that will form OH. In Fig. 2, we show a t-SNE map colored by the carbon to oxygen abundance ratio, from ASPCAP. Figure 2. View largeDownload slide A t-SNE map coloured by the carbon to oxygen abundance ratio, from ASPCAP. We focus on a cluster of carbon rich stars. We see that, according to a sequence visibly detectable on the map, objects at the high end of the sequence are not fitted by ASPCAP. Detecting these objects is possible using the similarity matrix. Figure 2. View largeDownload slide A t-SNE map coloured by the carbon to oxygen abundance ratio, from ASPCAP. We focus on a cluster of carbon rich stars. We see that, according to a sequence visibly detectable on the map, objects at the high end of the sequence are not fitted by ASPCAP. Detecting these objects is possible using the similarity matrix. Focusing on a cluster of carbon rich stars, we see that the objects are sequenced on the map according the C/O abundance ratio. Most importantly we see that a number of objects without pipeline abundance value are located at the top of the sequence. We suggest that the pipeline is not able to fit these objects due to them having extreme abundances, and due to the difficulty of abundance analysis of spectra with very strong molecular lines. We manually inspect the 51 objects with no ASPCAP value shown in Fig. 2 and see that they all show strong CN and weak OH, typical for carbon rich stars. Moreover, all of the 35 objects that have SIMBAD (Wenger et al. 2000) entry are classified as carbon stars, making the 15 objects without SIMBAD entry carbon star candidates. We note that many of these objects were observed as part of the APOGEE-2 AGB stars ancillary program, see Zasowski et al. (2017). This in an example of a query for objects based on their spectra, instead of on their fit parameters. This is of importance for objects with bad or non-existent fit parameters, that would otherwise be lost in the data set. In a large enough data set, it is very likely such objects will exist, these can be extreme cases of known phenomenon outside the range of the model, rare objects outside the scope of the model, or other types of outliers in the data set. 3.5 Spectroscopic twins The distance matrix produced by the algorithm can be used for finding objects with spectra similar to each other, objects sometimes referred to as spectroscopic twins. This is trivially achieved by sorting the distance matrix. One use of spectroscopic twins is measuring distances (Jofré et al. 2015). Twin stars will have the same luminosity, and if we know the distance to one of the stars (e.g. using parallax), we can calculate the distance to the other by comparing observed magnitudes. Jofré et al. (2015) looked for spectroscopic twins among 536 FGK stars, and detected 175 pairs with spectra indistinguishable within the errors. As we work with a rather homogeneous sample of ∼105 stars, we expect a large fraction of (multiple) twins. Example spectra for spectroscopic twins are shown in A1(b). It can be seen that the pairs have virtually identical spectra. Note that in the top example in Fig. A1(b) one of the spectra lacks an ASPCAP fit, preventing identification of a twin via these parameters. The middle pair have very similar parameters, while the bottom twins show more significantly different parameters. Our method finds them all, irrespectively. One thing we note is that this method for finding twins works well for the common object types (common in terms of representation in the data set), but might be less so for underrepresented types of objects (in which case we still get similar spectra, but not identical). The reason being that our unsupervised RF uses more extensively the features that are important for regular objects, and as a result it can separate objects based on subtle differences in these features. For other features that might not be important to most of the objects (for example, hydrogen absorption or emission), the RF uses cruder cuts to separate objects. As a test for the selection of twin objects, we look at the angular separation of stars with similar spectra. APOGEE stars living in the same environment are more likely to have similar physical properties, and thus similar spectra. We expect that pairs of stars we detect as having similar spectra will have higher probability to be located near each other compared with random pairs of stars. We note that there is an observational effect playing a role here – the APOGEE sample is not uniform in different parts of the galaxy (for example, dwarfs from the galactic bulge are too faint for APOGEE and are not observed). In Fig. 3, we show the distribution of the angular separation of each star in our sample with its nearest neighbour (as defined by our distance matrix), compared to random pairs of stars. Clearly, the twins we find are physically associated. We release our entire distance matrix, and we will update it with future data releases, allowing others to use the twins for further study. We note that our methodology does not allow one to compare well objects with different S/N, nor does it give a statistically meaningful similarity measure. However, it bypasses the difficulties of comparing spectra in data or model space, while producing very robust results. An additional example for using a distance metric to find similar objects is found in Jofré et al. (2017), who looked for nearest neighbours on a t-SNE map of RAVE stars in search for spectroscopic twins. To build the t-SNE map euclidian distance were used. Figure 3. View largeDownload slide A comparison between angular separation of pairs of stars detected as having similar spectra, and random pairs of stars. The similar spectra stars shown here are each star in our sample and its nearest neighbour according to our distance matrix. Figure 3. View largeDownload slide A comparison between angular separation of pairs of stars detected as having similar spectra, and random pairs of stars. The similar spectra stars shown here are each star in our sample and its nearest neighbour according to our distance matrix. 4 EFFICIENT OUTLIER DETECTION An important usage of the distance matrix is outlier detection. For this purpose, we calculate a weirdness score for each object in the sample. This weirdness score is calculated by summing over the distance matrix. In this section, we use the t-SNE visualization in order to understand the properties of this weirdness score. We present a new, local, definition of a weirdness score. We use this local weirdness score for APOGEE stars, and find it is more suitable for detecting outliers. We can use the t-SNE map in order learn about the weirdness score properties. In Fig. 1(e), we colour the t-SNE map by the weirdness score. The central, low Wall part of the t-SNE map contains about half of the objects in the sample. These objects are G and K giant stars, with weak molecular features in their spectra, but with prominent metallic features. They comprise one large group of objects with similar spectra. Below we refer to these objects as the main group. Example spectra for such objects are presented in Fig. A1(a). For each object in the figure, we present the percentile of the object's weirdness score, i.e. the percent of the objects with lower weirdness score. In order to better understand the properties of Wall, we examine its distribution in Fig. 4. The distribution decreases smoothly to high weirdness except for two bumps. We interpret the bumps as clusters of stars in our similarity space. One bump consists partially of low-temperature stars, and the other is due to stars with weak or non-existent absorption lines – metal poor stars and telluric calibration targets. The bumps in the distribution of Wall are due to the fact that there is one dominant cluster of objects in the data set, and the objects in smaller clusters receive a weirdness score based on how different they are from objects in the main cluster. These results might be useful to detect clusters, or to clean up the data set from objects outside the main group, but in order to find small classes of interesting outliers, we need a better outlier definition. Figure 4. View largeDownload slide Wall distribution for all objects in the sample. The two bumps in the distribution are composed mostly of objects with no ASPCAP fit parameters. Inspecting the spectra of these objects, we see that one bump contains low Teff stars, while the other contains low metallicity and hot telluric calibration stars (i.e. stars with weak or non-existent absorption lines). Figure 4. View largeDownload slide Wall distribution for all objects in the sample. The two bumps in the distribution are composed mostly of objects with no ASPCAP fit parameters. Inspecting the spectra of these objects, we see that one bump contains low Teff stars, while the other contains low metallicity and hot telluric calibration stars (i.e. stars with weak or non-existent absorption lines). To address the issue described above, we introduce the ‘nearest neighbours weirdness score’, a modification to the algorithm that produces ‘better outliers’ for the APOGEE data set. When looking for better outliers, we wish to get several different types of objects detected as outliers, in contrast to a weirdness score that strongly correlates to a single attribute (e.g. the effective temperature). In addition, we expect to be able to detect known outliers such as binaries and bad spectra. Instead of defining outliers based on their average distance to the entire sample, we use a more local measure, and for every object we calculate distances to its nearest neighbours. This measure of unusualness is used for distance-based outlier detection in other fields (Knorr & Ng 1999; Knorr, Ng & Tucakov 2000). The resulting weirdness score distribution is shown in Fig. 5. We can see that the bumps in the weirdness score distribution go away for a small enough number of nearest neighbours. When choosing the number of nearest neighbours to use in the weirdness score calculation, one can check at what point the weirdness score distribution does not contain bumps. Figure 5. View largeDownload slide Weirdness score distribution for different numbers of nearest neighbours included in the calculation. Figure 5. View largeDownload slide Weirdness score distribution for different numbers of nearest neighbours included in the calculation. A t-SNE map with the 250 nearest neighbours weirdness score (W250) is shown in Fig. 1(f). Clearly, there is a group of stars that have persistently high (percentile) weirdness score for any number of nearest neighbours used. On the other hand, the high Teff stars no longer have high weirdness score for small numbers of nearest neighbours. This results in various other groups of stars receiving higher percentile weirdness score. An open question regarding many outlier detection algorithms is setting a threshold on the weirdness score, i.e. determining above which weirdness score we mark an object as an ‘outlier’ and inspect it further. The t-SNE map could be of help here too: looking at the W250 t-SNE map (Fig. 1f), we can see that for each group of stars on the map, the edges receive higher weirdness score. We do not want to mark these edges as outliers, and from the t-SNE map we determine that this would be achieved with a threshold of 0.6, for this specific data set. The classification of the outliers is made easier by sorting the objects using their position on the t-SNE map. This way we can classify groups of similar objects instead of one object at a time. Another method we try for outlier inspection is called DEMUD (Wagstaff et al. 2013). Instead of examining the outliers sequentially, one starts from the weirdest object, and then inspects the weird object that is the farthest from the first, followed by the one farthest from the first two, and so forth. The idea is to sample the different populations of outliers quickly, stopping once we start seeing the same types of objects repeating. For the final classification of the outliers, we chose a threshold on the weirdness score (as discussed above) and use the t-SNE map to help with the classification. This is followed by taking a lower threshold on the weirdness score and using DEMUD to look for additional types of outliers. This second step did not result in new types of outliers. 5 APOGEE OUTLIERS In this section, we present the results of manual classification of the highest W250 stars. Here, we use results from both DR14 and DR13, as in DR14 many objects have poorly determined continua. In total, we look at 577 objects. The distribution of the different groups of outliers for DR13 only is shown in Fig. 6. We find the following large groups: Be stars, young stellar objects, carbon enriched stars, double lined spectroscopic binaries (SB2), fast rotators, M dwarfs, M giants and cool K giants (these stars have the highest Wall), and stars with bad spectra. In addition, we find a number of objects that do not fit into any of the above classes. Figure 6. View largeDownload slide Results of the manual classification of 348 highest W250. In the next sections, we discuss each of these groups. The non-GK giants group contains mostly M dwarfs. This figure refers to DR13. Figure 6. View largeDownload slide Results of the manual classification of 348 highest W250. In the next sections, we discuss each of these groups. The non-GK giants group contains mostly M dwarfs. This figure refers to DR13. ‘Bad spectra’ are objects with ASPCAP warn flags, combined with a strange looking spectrum. The flags we encounter for the outliers are commissioning, persist high, and persist jump neg(high). Other ‘bad spectra’ objects are not flagged but have faulty spectra. These appear only in DR14 and we discuss them below. We note that these classes are not mutually exclusive (in Fig. 6 each object is assigned to a single class we believe describes it best). 5.1 B-type emission line stars The objects in this group are Be stars. APOGEE targeted approximately 50 known Be stars in an ancillary program, while the additional Be stars in the APOGEE sample were originally targeted as telluric standard stars. Chojnowski et al. (2015a, 2017) compiled a catalogue of 238 Be stars in the APOGEE data set. They identified these stars by visual inspection. We find 40 Be stars not included in the Chojnowski et al. (2015a) catalogue. These new Be stars first appeared in DR14. For 26 of these stars emission was never reported before. We list these objects in table A2. Some of these stars were detected as outliers, while the rest were found by inspecting the neighbours, in the distance matrix and t-SNE map, of the outliers. As seen in Fig. A1(j), these stars have double peaked H-Br emission lines and weak absorption lines. For some Be stars, metallic emission is also present. ASPCAP fails to derive RVs for these objects, due to their unusual spectra. 5.2 Spectroscopic binaries Example spectra of SB2s are shown in Fig. A1(c) along with their best-fitting synthetic spectra. As seen, ASPCAP does not account for binarity. For some of the SB2s ASPCAP fits broad lines, and for others it fits only one of the two sets of lines. In general, the APOGEE reduction pipeline does not have an automatic binary identification routine (Nidever et al. 2015). Chojnowski et al. (2015b) compiled a catalogue of spectroscopic binaries in APOGEE. 2 The catalogue is constructed by searching for multiple peaks in the spectra cross-correlation function, when comparing to the synthetic template spectra. 15 of the 72 binaries we find as outliers are not listed in the catalogue of Chojnowski et al. (2015b) and are therefore new. 5.3 Fast rotators Broad line stars are also detected as outliers. ASPCAP fits broad lines well for dwarf stars but not for giants, as can be seen in Fig. A1(d). These stars are all flagged with suspect broad lines by the ASPCAP pipeline. 5.4 Carbon rich stars Carbon rich stars (discussed in Section 3.4) are also detected as outliers. In Fig. A1(e), we can see the strong CN compared to OH lines for a few carbon enriched stars. The weirdness score increases with the strength of the CN features. 5.5 Young stellar objects Stars in this group show both H-Br emission lines as well as regular metallic absorption lines. They are mostly young stars included in the INfrared Survey of Young Nebulous Clusters (IN-SYNC, Cottaar et al. 2014). We detect stars with both broad and narrow emission, and with absorption that can be broad or narrow as well as double lined (SB2s). SIMBAD classification for stars in this group include ‘Variable star of Orion type’, ‘T Tau-type Star’, ‘Pre-main sequence Star’, and ‘Young stellar object’. 5.6 M dwarfs M dwarf stars are also detected as outliers. This is due to the small number of M dwarf stars in the APOGEE sample. Example spectra are in Fig. A1(g). 5.7 Other outliers Some of the objects detected as outliers did not fall into any of the above classes. These include a brown dwarf, a Wolf–Rayet star, a few AGB stars including an OH-IR star, and known variable stars, as well as three red supergiants observed in the massive stars ancillary program. Also detected as outliers are special non-stellar targets, such as the centre of M32, a few M31 globular clusters, and three planetary nebulae. Two outliers show double peaked H-Br emission lines, as well as absorption lines typical to the APOGEE data set. Both of these objects show RV modulations, suggesting they are multiple star systems. For the first, 2M04052624+5304494, the RV modulation of the absorption lines (determined from APOGEE visit spectra) could be modelled with a period of P = 11.152 ± 0.072 d, and amplitude of K = 78 ± 15 km s−1. The emission lines in the APOGEE spectra show smaller RV modulation, if any. For the second, 2M06415063−0130177, the absorption lines RV changes by ∼160 km s−1 between two APOGEE visits. The visits are separated by 28 d. For the emission lines we could not get a good estimate on the RVs, as the emission line profiles change significantly between the visits. A CoRoT light-curve is available for this system, showing clear periodic modulation. A period of P = 29.04 d was derived for this light-curve by Affer et al. (2012). We note that this period does not agree with the RV modulation. For both of these systems, additional work is required to determine their nature. We also detect a group of objects with similar, very broad features. Most of these objects have SIMBAD classifications as contact binaries, mainly W Ursae Majoris. A few objects remain unexplained. We divide these objects into two groups. In the first group, we have objects with spectra that seems to have similar features to typical APOGEE red giants (by means of visual inspection). The second group contains stars with spectra that are clearly different from typical APOGEE red giants. We refer to the first group as unexplained red giants, and to the second group as unexplained non-red giants. Some of the unexplained red giants stars have low carbon and high nitrogen ASPCAP abundances. Inspecting their spectra, we do see significantly weaker CO features relative to low weirdness score stars with similar stellar parameters. One of these stars, 2M17534571−2949362, is discussed in Fernández-Trincado et al. (2017) as having low Mg, but high Al and N abundances. There are three unexplained non-red giants, the first is 2M03411288+2453344, which was targeted as a telluric calibrator target. As could be seen in Fig. A1(h), the ASPCAP fit does not catch many of the features in the spectrum, in particular, there is no H-Br absorption. The cross-correlation function shows a single peak, suggesting it is not a binary star. There are three visits to this star, all showing the same features. The objects most similar to this one, according to the distance matrix, do not show similar features. 2M05264478+1049152 has very broad features that we do not identify. Same goes for 2M23375653+8534449, which also has a single emission line centred at $$\lambda = 16055 [{^{\circ}_{\rm A}}]$$ that we cannot identify. We show the spectra of the unexplained non-red giants in Fig. 7. Figure 7. View largeDownload slide Spectra for the three unclassified outliers. Top spectrum is a typical APOGEE red giant, for comparison. The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. Figure 7. View largeDownload slide Spectra for the three unclassified outliers. Top spectrum is a typical APOGEE red giant, for comparison. The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. 5.8 Bad reductions In DR14, roughly half of the high weirdness score objects have badly determined continua. We show a few examples in Fig. A1. These objects can be divided into two groups. For the first group, the issue seems to be a bug in the ASPCAP PCN process. For objects in this group the combined unnormalized spectra looks regular, as well as the DR13 PCN spectra (for objects with available DR13 data). For the second group already at least one of the visit spectra is faulty, and this error propagates down the pipeline. Examples for both of these errors are presented in Fig. A1(i). 6 SUMMARY In this work, we calculate a similarity measure for APOGEE infrared spectra of stars. We show that this similarity matrix traces physical properties such as effective temperature, metallicity, and surface gravity. Such a similarity matrix could be used for object retrieval, i.e. finding objects that are similar to a given example, it can be used to detect outliers, and more generally to assist learning about the structure of a data set. The similarity is obtained without inputing information derived by model fitting, and thus the similarity could be used to query and learn about objects that are not well fitted by the pipeline and as such are hard to find using the fit parameters data base. As noted above, we find that the unsupervised RF is capable of aggregating complex spectral information into a single number, the pair-wise distance between two objects. We find that various stellar parameters are encoded into this distance, and that the resulting RF represents a general model of stellar spectra [Baron & Poznanski (2017) showed that this is true for spectra of galaxies]. As such, one can imagine inverting the process, and using the trained RF to generate ‘real-looking’ objects, which is in turn a generative model. Using this unsupervised RF distance matrix and dimensionality reduction techniques, one can study the structure of the data, and the relations between different classes of objects within a data set. However, it is worth noting that much of the insight gained about the APOGEE sample was made possible using the derived ASPCAP stellar parameters. Without these labels, coloring the t-SNE map would not have been possible. While our proposed unsupervised distance matrix contains various types of information, the extraction of this information still heavily depends on annotations of the distance matrix. Thus, for many applications, it is only the combination of our approach and existing knowledge about the data set that can be useful to gain additional insight. Using our distance matrix to detect outliers, we find objects from the following types of known classes: B-type emission-line stars, carbon rich stars, spectroscopic binaries, broad line stars, young stars, bad spectra, and M dwarfs (which are ordinary but underrepresented in the data set), showing that the algorithm is capable of detecting a wide variety of phenomena. A few dozens of objects that were detected as outliers did not fall into any of the large groups, these include special targets such as galaxies, globular clusters, and planetary nebulae, stars with unusual abundances, contact binaries, stars observed with the massive star ancillary program and more. Three outliers remain without explanation. Some of the carbon rich outliers have a poor ASPCAP fit, though these groups are included in the ASPCAP stellar spectral library. Possibly the objects without a good fit are extreme cases and could be used to improve and test the pipeline. The SB2s detected as outliers have diverse types of spectra and could be used to test SB2 detection specific algorithms. Bad spectra objects and underrepresented objects are not interesting by themselves, but detecting them could be useful in order to clean the sample and find bugs in the pipeline. Finding new Be stars is an example for detection of new objects of known types using the distance matrix or t-SNE map. This is especially useful in larger surveys, where visual inspection is not feasible. The use of t-SNE to visualize the distance matrix was also useful for the purpose of outlier detection. This enabled us to speed up the classification of the outliers by classifying nearby objects together. More importantly, the t-SNE map proved to be useful in learning about the regular objects in the data set, an important step to take before looking at the outliers. Viewing spectra of objects located in different regions of the t-SNE map allowed us to quickly review the different classes of regular objects. For the APOGEE data set, in which there is one large group of similar objects, a nearest neighbours weirdness score, or a ‘local’ weirdness score, was needed in order to detect the interesting outliers. Although this was not required to detect the interesting outlying galaxies in Baron & Poznanski (2017), we believe the local weirdness score is more general and should be used in future work. The number of nearest neighbours to use when calculating the local weirdness score is data set dependent. Coloring the t-SNE map by the different weirdness scores or building t-SNE maps with different perplexities, can help decide on which number of nearest neighbours is appropriate. It is also possible that in order to detect all interesting objects one type of nearest neighbours weirdness score would not be enough, as different types of outliers can come in different (small) cluster sizes. In our case, the outliers population seemed robust to a number of nearest neighbours from a few to a few thousands. We note that for the map shown in Fig. 1, we used perplexity of 2000. This value was chosen in order to make the visualization relatively simple. With smaller perplexity, we obtained maps with more complex small scale structure, such as small clusters. These maps could be useful for investigating the data further but for a clean visualization of the large-scale structure we used a high perplexity map. Future work could involve combining the distance matrix, which is based on spectral data alone, with other types of available data. A natural direction is the physical position of a star. For example, one can look for stars that are normal compared to the entire population of stars, but are weird when compared to their local environment. A table with the 100 nearest neighbours of each object, including their respective distances, is available online. We also include the coordinates for the t-SNE map shown above. Acknowledgements We thank D. Hogg for suggesting the use of t-SNE, and other useful comments, and D. Chojnowski for discussing some of the outliers. We also thank the reviewer for helpful suggestions to improve this manuscript. This research made use of: the NASA Astrophysics Data System Bibliographic Services, scikit-learn (Pedregosa et al. 2011), SciPy (Jones et al. 2001), IPython (Pérez & Granger 2007), matplotlib (Hunter 2007), astropy (Astropy Collaboration et al. 2013), and the SIMBAD data base (Wenger et al. 2000). This work made extensive use of SDSS data. Funding for the SDSS IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. Footnotes 1 Code can be found at https://github.com/dalya/WeirdestGalaxies 2 Their catalogue can be found here http://astronomy.nmsu.edu/drewski/apogee-sb2/apSB2.html REFERENCES Abolfathi B. et al.  , 2017, preprint (arXiv:1707.09322) Affer L., Micela G., Favata F., Flaccomio E., 2012, MNRAS , 424, 11 https://doi.org/10.1111/j.1365-2966.2012.20802.x CrossRef Search ADS   Astropy C ollaboration et al.  , 2013, A&A , 558, A33 https://doi.org/10.1051/0004-6361/201322068 CrossRef Search ADS   Ball N. M., Brunner R. J., 2010, Int. J. Mod. Phys. D , 19, 1049 https://doi.org/10.1142/S0218271810017160 CrossRef Search ADS   Baron D., Poznanski D., 2017, MNRAS , 465, 4530 https://doi.org/10.1093/mnras/stw3021 CrossRef Search ADS   Baron D., Poznanski D., Watson D., Yao Y., Cox N. L. J., Prochaska J. X., 2015, MNRAS , 451, 332 https://doi.org/10.1093/mnras/stv977 CrossRef Search ADS   Baron D., Netzer H., Poznanski D., Prochaska J. X., Förster Schreiber N. M., 2017, MNRAS , 470, 1687 https://doi.org/10.1093/mnras/stx1329 CrossRef Search ADS   Bloom J. S. et al.  , 2012, PASP , 124, 1175 https://doi.org/10.1086/668468 CrossRef Search ADS   Bovy J., 2016, ApJ , 817, 49 https://doi.org/10.3847/0004-637X/817/1/49 CrossRef Search ADS   Bovy J. et al.  , 2014, ApJ , 790, 127 https://doi.org/10.1088/0004-637X/790/2/127 CrossRef Search ADS   Breiman L., 2001, Machine Learning , 45, 5 https://doi.org/10.1023/A:1010933404324 CrossRef Search ADS   Breiman L., Cutler A., 2003, Tech. Rep , Available at: https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf. Breiman L., Friedman J., Olshen R., Stone C., 1984, Classification and Regression Trees . Wadsworth and Brooks, Monterey, CA Chiappini C. et al.  , 2015, A&A , 576, L12 https://doi.org/10.1051/0004-6361/201525865 CrossRef Search ADS   Chojnowski S. D. et al.  , 2015a, AJ , 149, 7 https://doi.org/10.1088/0004-6256/149/1/7 CrossRef Search ADS   Chojnowski S. D. et al.  , 2015b, Am. Astron. Soc. Meeting Abstr. , p. 340 Chojnowski S. D. et al.  , 2017, AJ , 153, 174 https://doi.org/10.3847/1538-3881/aa64ce CrossRef Search ADS   Cottaar M. et al.  , 2014, ApJ , 794, 125 https://doi.org/10.1088/0004-637X/794/2/125 CrossRef Search ADS   Eisenstein D. J. et al.  , 2011, AJ , 142, 72 https://doi.org/10.1088/0004-6256/142/3/72 CrossRef Search ADS   Fernández-Trincado J. G. et al.  , 2017, ApJ , 846, L2 https://doi.org/10.3847/2041-8213/aa8032 CrossRef Search ADS   Frinchaboy P. M. et al.  , 2013, ApJ , 777, L1 https://doi.org/10.1088/2041-8205/777/1/L1 CrossRef Search ADS   García Pérez A. E. et al.  , 2016, AJ , 151, 144 https://doi.org/10.3847/0004-6256/151/6/144 CrossRef Search ADS   Garcia-Dias R., Allende Prieto C., Sánchez Almeida J., Ordovás-Pascual I., 2018, preprint (arXiv:1801.07912) Hayden M. R. et al.  , 2015, ApJ , 808, 132 https://doi.org/10.1088/0004-637X/808/2/132 CrossRef Search ADS   Hunter J. D., 2007, Comput. Sci. Eng. , 9, 90 https://doi.org/10.1109/MCSE.2007.55 CrossRef Search ADS   Jofré P., Mädler T., Gilmore G., Casey A. R., Soubiran C., Worley C., 2015, MNRAS , 453, 1428 https://doi.org/10.1093/mnras/stv1724 CrossRef Search ADS   Jofré P. et al.  , 2017, MNRAS , 472, 2517 https://doi.org/10.1093/mnras/stx1877 CrossRef Search ADS   Jones E. et al.  , 2001, SciPy: Open source scientific tools for Python , Available at: http://www.scipy.org/ Knorr E. M., Ng R. T., 1999, in Proceedings of the 25th International Conference on Very Large Data Bases. VLDB’99 . Morgan Kaufmann Publishers Inc. , San Francisco, CA, USA, pp 211– 222. Available at: http://dl.acm.org/citation.cfm?id=645925.671529 Knorr E. M., Ng R. T., Tucakov V., 2000, The VLDB Journal , 8, 237 https://doi.org/10.1007/s007780050006 CrossRef Search ADS   Majewski S. R., APOGEE Team APOGEE-2 Team, 2016, Astron. Nachr. , 337, 863 https://doi.org/10.1002/asna.201612387 CrossRef Search ADS   Masci F. J., Hoffman D. I., Grillmair C. J., Cutri R. M., 2014, AJ , 148, 21 https://doi.org/10.1088/0004-6256/148/1/21 CrossRef Search ADS   Meusinger H., Schalldach P., Scholz R.-D., in der Au A., Newholm M., de Hoon A., Kaminsky B., 2012, A&A , 541, A77 https://doi.org/10.1051/0004-6361/201118143 CrossRef Search ADS   Miller A. A., Kulkarni M. K., Cao Y., Laher R. R., Masci F. J., Surace J. A., 2017, AJ , 153, 73 https://doi.org/10.3847/1538-3881/153/2/73 CrossRef Search ADS   Ness M., Hogg D. W., Rix H.-W., Ho A. Y. Q., Zasowski G., 2015, ApJ , 808, 16 https://doi.org/10.1088/0004-637X/808/1/16 CrossRef Search ADS   Nidever D. L. et al.  , 2014, ApJ , 796, 38 https://doi.org/10.1088/0004-637X/796/1/38 CrossRef Search ADS   Nidever D. L. et al.  , 2015, AJ , 150, 173 https://doi.org/10.1088/0004-6256/150/6/173 CrossRef Search ADS   Pedregosa F. et al.  , 2011, J. Mach. Learn. Res. , 12, 2825 Pérez F., Granger B. E., 2007, Comput. Sci. Eng. , 9, 21 CrossRef Search ADS   Pimentel M. A., Clifton D. A., Clifton L., Tarassenko L., 2014, Signal Process. , 99, 215 https://doi.org/10.1016/j.sigpro.2013.12.026 CrossRef Search ADS   Schawinski K., Zhang C., Zhang H., Fowler L., Santhanam G. K., 2017, MNRAS , 467, L110 Shi T., Horvath S., 2006, J. Comput. Graph. Stat. , 15, 118 https://doi.org/10.1198/106186006X94072 CrossRef Search ADS   van der Maaten L. J. P., Hinton G. E., 2008, J. Mach. Learn. Res. , 9, 2579 Wagstaff K. L., Lanza N. L., Thompson D. R., Dietterich T. G., Gilmore M. S., 2013, in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI’13 . AAAI Press, pp 905– 911. Available at: http://dl.acm.org/citation.cfm?id=2891460.2891586 Wattenberg M., Viégas F., Johnson I., 2016, Distill  Wenger M. et al.  , 2000, A&AS , 143, 9 CrossRef Search ADS   Yang L., 2006, Distance Metric Learning: A Comprehensive Survey . Available at: http://www.cs.cmu.edu/~liuy/frame_survey_v2.pdf Zasowski G. et al.  , 2013, AJ , 146, 81 https://doi.org/10.1088/0004-6256/146/4/81 CrossRef Search ADS   Zasowski G. et al.  , 2017, AJ , 154, 198 https://doi.org/10.3847/1538-3881/aa8df9 CrossRef Search ADS   APPENDIX A: SPECTRA AND TABLES In Fig. A1, we show example spectra of objects from the different outlying groups, as well as spectroscopic twins. Figure A1. View largeDownload slide Example spectra for different groups of objects. The spectra plots were made using the APOGEE toolkit by Bovy (2016). The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. In every panel, we choose the most informative wavelength range. (a) Regular objects, i.e. objects with low weirdness scores. Clearly, all have similar spectra. PR(W250) indicates the percentile of objects with lower weirdness score. (b) Three example pairs of spectroscopic twins. The twin spectra are over-plotted, one in green and the other in blue. Note that in the top example one of the stars lacks an ASPCAP fit, preventing identification as a twin via these parameters. (c) Spectroscopic binaries. (d) Fast rotators. Top spectrum is a typical APOGEE red giant, for comparison. (e) Carbon enriched stars. Top spectrum is a typical APOGEE red giant, for comparison. (f) Stars with both absorption and hydrogen emission. Top spectrum is a typical APOGEE red giant, for comparison. We see both narrow and broad emission stars, and also both narrow and broad absorption. The second spectra from the top is also an SB2. The bottom spectrum has bad RV determination. (g) M dwarfs. Top spectrum is a typical APOGEE red giant, for comparison. M dwarfs are detected as outliers due to their underrepresentation in the APOGEE data set. (h) Stars from the ‘others’ pile. Top spectrum is a typical APOGEE red giant, for comparison. Starting from the second from top, the four outlying spectra are brown dwarf, massive star target, unexplained red giant, and a Wolf–Rayet star. (i)DR14 Faulty spectra. For the top two objects, the problems are due to an issue in the PCN process, for the bottom three one of the visit spectra is bad. (j) B-type emission line stars showing double peaked hydrogen emission. The emission lines are not on the dotted lines due to wrong RV determination by the APOGEE pipeline. Figure A1. View largeDownload slide Example spectra for different groups of objects. The spectra plots were made using the APOGEE toolkit by Bovy (2016). The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. In every panel, we choose the most informative wavelength range. (a) Regular objects, i.e. objects with low weirdness scores. Clearly, all have similar spectra. PR(W250) indicates the percentile of objects with lower weirdness score. (b) Three example pairs of spectroscopic twins. The twin spectra are over-plotted, one in green and the other in blue. Note that in the top example one of the stars lacks an ASPCAP fit, preventing identification as a twin via these parameters. (c) Spectroscopic binaries. (d) Fast rotators. Top spectrum is a typical APOGEE red giant, for comparison. (e) Carbon enriched stars. Top spectrum is a typical APOGEE red giant, for comparison. (f) Stars with both absorption and hydrogen emission. Top spectrum is a typical APOGEE red giant, for comparison. We see both narrow and broad emission stars, and also both narrow and broad absorption. The second spectra from the top is also an SB2. The bottom spectrum has bad RV determination. (g) M dwarfs. Top spectrum is a typical APOGEE red giant, for comparison. M dwarfs are detected as outliers due to their underrepresentation in the APOGEE data set. (h) Stars from the ‘others’ pile. Top spectrum is a typical APOGEE red giant, for comparison. Starting from the second from top, the four outlying spectra are brown dwarf, massive star target, unexplained red giant, and a Wolf–Rayet star. (i)DR14 Faulty spectra. For the top two objects, the problems are due to an issue in the PCN process, for the bottom three one of the visit spectra is bad. (j) B-type emission line stars showing double peaked hydrogen emission. The emission lines are not on the dotted lines due to wrong RV determination by the APOGEE pipeline. Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued In Table A1, we present all the objects detected as outliers, and did not fall into any of the large groups. Tables with the objects in the rest of the groups are available online. Table A1. Outliers that and did not fall into any of the large groups. APOGEE ID  RA (°)  Dec. (°)  Classification  2M15010818+2250020  225.284  22.8339  Brown dwarf  2M14323054+5049406  218.127  50.828  Contact binary  2M03114116−0043477  47.9215  −0.72993  Contact binary  2M14304297+0905087  217.679  9.08575  Contact binary  2M13465180+2257140  206.716  22.9539  Contact binary  2M14120965+0508201  213.04  5.13893  Contact binary  2M16145863+3016356  243.744  30.2766  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M16241043+4555265  246.043  45.924  Contact binary  2M16524137+4723275  253.172  47.391  Contact binary  2M06415063−0130177  100.461  −1.50494  Double-peaked emission  2M04052624+5304494  61.3594  53.0804  Double-peaked emission  2M13145725+1713303  198.739  17.2251  Galaxy  AP00425080+4117074  10.7117  41.2854  Globular cluster  AP00442956+4121359  11.1232  41.36  Globular cluster  AP00430957+4121321  10.7899  41.3589  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00431764+4127450  10.8235  41.4625  Globular cluster  2M18445087−0325251  281.212  −3.42364  Massive star  2M18452141−0330149  281.339  −3.50416  Massive star  2M18440079-0353160  281.003  −3.88778  Massive star  2M03411288+2453344  55.3037  24.8929  Unexplained non-red giant  2M05264478+1049152  81.6866  10.8209  Unexplained non-red giant  2M23375653+8534449  354.486  85.5792  Unexplained non-red giant  2M04255084+6007127  66.4619  60.1202  Planetary nebula  2M21021878+3641412  315.578  36.6948  Planetary nebula–Egg nebula  2M18211606−1301256  275.317  −13.0238  Planetary nebula–Red Square nebula  2M17534571−2949362  268.44  −29.8267  Unexplained red giant  2M06361326+0919120  99.0553  9.32001  Unexplained red giant  2M00220008+6915238  5.50037  69.2566  Unexplained red giant  2M21184119+4836167  319.672  48.6047  Unexplained red giant  2M20564714+5013372  314.196  50.227  Unexplained red giant  2M05501847−0010369  87.577  −0.176939  Unexplained red giant  2M23001010+6055385  345.042  60.9274  Wolf–Rayet star  2M05473667+0020060  86.9028  0.33501  Young stellar object  APOGEE ID  RA (°)  Dec. (°)  Classification  2M15010818+2250020  225.284  22.8339  Brown dwarf  2M14323054+5049406  218.127  50.828  Contact binary  2M03114116−0043477  47.9215  −0.72993  Contact binary  2M14304297+0905087  217.679  9.08575  Contact binary  2M13465180+2257140  206.716  22.9539  Contact binary  2M14120965+0508201  213.04  5.13893  Contact binary  2M16145863+3016356  243.744  30.2766  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M16241043+4555265  246.043  45.924  Contact binary  2M16524137+4723275  253.172  47.391  Contact binary  2M06415063−0130177  100.461  −1.50494  Double-peaked emission  2M04052624+5304494  61.3594  53.0804  Double-peaked emission  2M13145725+1713303  198.739  17.2251  Galaxy  AP00425080+4117074  10.7117  41.2854  Globular cluster  AP00442956+4121359  11.1232  41.36  Globular cluster  AP00430957+4121321  10.7899  41.3589  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00431764+4127450  10.8235  41.4625  Globular cluster  2M18445087−0325251  281.212  −3.42364  Massive star  2M18452141−0330149  281.339  −3.50416  Massive star  2M18440079-0353160  281.003  −3.88778  Massive star  2M03411288+2453344  55.3037  24.8929  Unexplained non-red giant  2M05264478+1049152  81.6866  10.8209  Unexplained non-red giant  2M23375653+8534449  354.486  85.5792  Unexplained non-red giant  2M04255084+6007127  66.4619  60.1202  Planetary nebula  2M21021878+3641412  315.578  36.6948  Planetary nebula–Egg nebula  2M18211606−1301256  275.317  −13.0238  Planetary nebula–Red Square nebula  2M17534571−2949362  268.44  −29.8267  Unexplained red giant  2M06361326+0919120  99.0553  9.32001  Unexplained red giant  2M00220008+6915238  5.50037  69.2566  Unexplained red giant  2M21184119+4836167  319.672  48.6047  Unexplained red giant  2M20564714+5013372  314.196  50.227  Unexplained red giant  2M05501847−0010369  87.577  −0.176939  Unexplained red giant  2M23001010+6055385  345.042  60.9274  Wolf–Rayet star  2M05473667+0020060  86.9028  0.33501  Young stellar object  View Large In Table A2, we list Be stars which are new in DR14 and thus not included in the Chojnowski et al. (2015a) catalogue. Table A2. Be stars. APOGEE ID  RA (°)  Dec. (°)  2M20383016+2119439  309.626  21.3289  2M22425730+4443183  340.739  44.7218  2M06490825+0005220  102.284  0.089448  2M04480651+3359160  72.0271  33.9878  2M05284845+0209529  82.2019  2.16471  2M21582976+5429057  329.624  54.4849  2M22082542+5413262  332.106  54.2239  2M19322817−0454283  293.117  −4.90786  2M03145531+4841448  48.7305  48.6958  2M21523408+4713436  328.142  47.2288  2M04454937+4323302  71.4557  43.3917  2M18574904+1758251  284.454  17.9736  2M05312677+1101226  82.8616  11.0229  2M05384719−0235405  84.6967  −2.59459  2M22075623+5431064  331.984  54.5185  2M21380289+5037030  324.512  50.6175  2M06521036-0017440  103.043  −0.29556  2M04125427+6647203  63.2262  66.789  2M22142219+4206020  333.592  42.1006  2M04493134+3313091  72.3806  33.2192  2M02374876+5248458  39.4532  52.8127  2M05122466+4816538  78.1028  48.2816  2M23570808+6118272  359.284  61.3076  2M05441926+5241437  86.0803  52.6955  2M18042714−0958113  271.113  −9.96982  2M06514059+0019363  102.919  0.326773  2M02273460+4813548  36.8942  48.2319  2M23293672+4822513  352.403  48.3809  2M18040936−0827329  271.039  −8.45915  2M21151579+3235270  318.816  32.5909  2M06552851+2430188  103.869  24.5052  2M19575932+2714001  299.497  27.2334  2M04563331+6345566  74.1388  63.7657  2M19562230+2626258  299.093  26.4405  2M10214707+1532036  155.446  15.5344  2M05271779+1308569  81.8241  13.1492  2M02571539+4601118  44.3142  46.02  2M21504079+5518451  327.67  55.3125  2M04503901+3243187  72.6626  32.7219  2M22165865+6738450  334.244  67.6458  APOGEE ID  RA (°)  Dec. (°)  2M20383016+2119439  309.626  21.3289  2M22425730+4443183  340.739  44.7218  2M06490825+0005220  102.284  0.089448  2M04480651+3359160  72.0271  33.9878  2M05284845+0209529  82.2019  2.16471  2M21582976+5429057  329.624  54.4849  2M22082542+5413262  332.106  54.2239  2M19322817−0454283  293.117  −4.90786  2M03145531+4841448  48.7305  48.6958  2M21523408+4713436  328.142  47.2288  2M04454937+4323302  71.4557  43.3917  2M18574904+1758251  284.454  17.9736  2M05312677+1101226  82.8616  11.0229  2M05384719−0235405  84.6967  −2.59459  2M22075623+5431064  331.984  54.5185  2M21380289+5037030  324.512  50.6175  2M06521036-0017440  103.043  −0.29556  2M04125427+6647203  63.2262  66.789  2M22142219+4206020  333.592  42.1006  2M04493134+3313091  72.3806  33.2192  2M02374876+5248458  39.4532  52.8127  2M05122466+4816538  78.1028  48.2816  2M23570808+6118272  359.284  61.3076  2M05441926+5241437  86.0803  52.6955  2M18042714−0958113  271.113  −9.96982  2M06514059+0019363  102.919  0.326773  2M02273460+4813548  36.8942  48.2319  2M23293672+4822513  352.403  48.3809  2M18040936−0827329  271.039  −8.45915  2M21151579+3235270  318.816  32.5909  2M06552851+2430188  103.869  24.5052  2M19575932+2714001  299.497  27.2334  2M04563331+6345566  74.1388  63.7657  2M19562230+2626258  299.093  26.4405  2M10214707+1532036  155.446  15.5344  2M05271779+1308569  81.8241  13.1492  2M02571539+4601118  44.3142  46.02  2M21504079+5518451  327.67  55.3125  2M04503901+3243187  72.6626  32.7219  2M22165865+6738450  334.244  67.6458  View Large In Table A3, we list carbon rich stars that were detected as outliers. Table A3. Carbon rich stars. APOGEE ID  RA (°)  Dec. (°)  2M21095891+1111013  317.495  11.1837  2M12553245+4328014  193.885  43.4671  2M08031240+5311340  120.802  53.1928  2M17552511−2517291  268.855  −25.2914  2M18442763−0614402  281.115  −6.24452  2M06211564−0124429  95.3152  −1.41194  2M12410240−0853066  190.26  −8.88517  2M13150364+1806426  198.765  18.1119  2M07384226+2131021  114.676  21.5173  2M05264861+2551545  81.7026  25.8652  2M16334467−1343201  248.436  −13.7223  2M13381781−1458456  204.574  −14.9793  2M13122536+1313575  198.106  13.2327  2M15000319+2955500  225.013  29.9306  2M21330683+1209406  323.278  12.1613  2M18455347−0328585  281.473  −3.48293  2M18495015−0235162  282.459  −2.58786  2M19425134+2235573  295.714  22.5993  2M19474632+2349074  296.943  23.8187  2M00242588+6221034  6.10785  62.3509  2M04501927+3947587  72.5803  39.7996  2M05012902+4023388  75.3709  40.3941  2M21053099+2952201  316.379  29.8723  2M01403590+6254392  25.1496  62.9109  2M04405098+4705190  70.2124  47.0886  2M18191371−1218145  274.807  −12.304  2M18030503−2157460  270.771  −21.9628  2M18015024−2638220  270.459  −26.6395  2M17520031−2308488  268.001  −23.1469  2M18052874−2505351  271.37  −25.0931  2M18063056−2435442  271.627  −24.5956  2M18111704−2352577  272.821  −23.8827  2M18115753−1503100  272.99  −15.0528  2M18185547−1119080  274.731  −11.3189  2M18524968−2834454  283.207  −28.5793  2M17301939−2913292  262.581  −29.2248  2M19295061+0010102  292.461  0.16951  2M19411240+4936344  295.302  49.6096  2M19003459+4408290  285.144  44.1414  2M19023427+4246148  285.643  42.7708  2M19095794+4325272  287.491  43.4242  2M06343313+0643006  98.6381  6.71686  2M01471583+5753060  26.816  57.885  2M21473632+5932259  326.901  59.5405  2M21544864+5916346  328.703  59.2763  2M11475977−0019182  176.999  −0.32173  2M18284700−1010553  277.196  −10.182  2M17043371−2212322  256.14  −22.209  2M19233187+4405575  290.883  44.0993  2M12553245+4328014  193.885  43.4671  2M14561660+1702441  224.069  17.0456  2M15015733+2713595  225.489  27.2332  2M15100330+3054073  227.514  30.902  2M23290070+5711558  352.253  57.1989  2M02403149+5600473  40.1312  56.0132  2M07094794+0006382  107.45  0.11062  2M07232483−0823577  110.853  −8.39936  2M03463234+3221127  56.6348  32.3536  2M19531095+4635518  298.296  46.5977  2M19200927+1317078  290.039  13.2855  2M21315424+5219122  322.976  52.3201  2M00334926+6837330  8.45529  68.6258  2M04174731+4211335  64.4472  42.1926  2M04195310+4109094  64.9713  41.1526  2M06372981+0515011  99.3742  5.25033  2M00373109+5743345  9.37956  57.7263  2M03244820+6300289  51.2009  63.008  2M03271166+6240211  51.7986  62.6725  2M03330010+6330443  53.2504  63.5123  2M03395727+6227241  54.9886  62.4567  2M06393827+2403560  99.9095  24.0656  2M05152962+2400147  78.8734  24.0041  2M03103113+4831002  47.6297  48.5167  2M03221451+4756591  50.5605  47.9498  2M05522651+4329557  88.1105  43.4988  2M20553607+5613011  313.9  56.217  2M21031081+5414127  315.795  54.2369  2M21084459+5442122  317.186  54.7034  2M21590597+4539010  329.775  45.6503  2M21554492+5414593  328.937  54.2498  2M21573025+5440529  329.376  54.6814  2M21594113+5351121  329.921  53.8534  2M22085910+5434192  332.246  54.572  2M18300408+0416050  277.517  4.26807  2M04113023+2255071  62.876  22.9187  2M06531594−0439506  103.316  −4.66407  APOGEE ID  RA (°)  Dec. (°)  2M21095891+1111013  317.495  11.1837  2M12553245+4328014  193.885  43.4671  2M08031240+5311340  120.802  53.1928  2M17552511−2517291  268.855  −25.2914  2M18442763−0614402  281.115  −6.24452  2M06211564−0124429  95.3152  −1.41194  2M12410240−0853066  190.26  −8.88517  2M13150364+1806426  198.765  18.1119  2M07384226+2131021  114.676  21.5173  2M05264861+2551545  81.7026  25.8652  2M16334467−1343201  248.436  −13.7223  2M13381781−1458456  204.574  −14.9793  2M13122536+1313575  198.106  13.2327  2M15000319+2955500  225.013  29.9306  2M21330683+1209406  323.278  12.1613  2M18455347−0328585  281.473  −3.48293  2M18495015−0235162  282.459  −2.58786  2M19425134+2235573  295.714  22.5993  2M19474632+2349074  296.943  23.8187  2M00242588+6221034  6.10785  62.3509  2M04501927+3947587  72.5803  39.7996  2M05012902+4023388  75.3709  40.3941  2M21053099+2952201  316.379  29.8723  2M01403590+6254392  25.1496  62.9109  2M04405098+4705190  70.2124  47.0886  2M18191371−1218145  274.807  −12.304  2M18030503−2157460  270.771  −21.9628  2M18015024−2638220  270.459  −26.6395  2M17520031−2308488  268.001  −23.1469  2M18052874−2505351  271.37  −25.0931  2M18063056−2435442  271.627  −24.5956  2M18111704−2352577  272.821  −23.8827  2M18115753−1503100  272.99  −15.0528  2M18185547−1119080  274.731  −11.3189  2M18524968−2834454  283.207  −28.5793  2M17301939−2913292  262.581  −29.2248  2M19295061+0010102  292.461  0.16951  2M19411240+4936344  295.302  49.6096  2M19003459+4408290  285.144  44.1414  2M19023427+4246148  285.643  42.7708  2M19095794+4325272  287.491  43.4242  2M06343313+0643006  98.6381  6.71686  2M01471583+5753060  26.816  57.885  2M21473632+5932259  326.901  59.5405  2M21544864+5916346  328.703  59.2763  2M11475977−0019182  176.999  −0.32173  2M18284700−1010553  277.196  −10.182  2M17043371−2212322  256.14  −22.209  2M19233187+4405575  290.883  44.0993  2M12553245+4328014  193.885  43.4671  2M14561660+1702441  224.069  17.0456  2M15015733+2713595  225.489  27.2332  2M15100330+3054073  227.514  30.902  2M23290070+5711558  352.253  57.1989  2M02403149+5600473  40.1312  56.0132  2M07094794+0006382  107.45  0.11062  2M07232483−0823577  110.853  −8.39936  2M03463234+3221127  56.6348  32.3536  2M19531095+4635518  298.296  46.5977  2M19200927+1317078  290.039  13.2855  2M21315424+5219122  322.976  52.3201  2M00334926+6837330  8.45529  68.6258  2M04174731+4211335  64.4472  42.1926  2M04195310+4109094  64.9713  41.1526  2M06372981+0515011  99.3742  5.25033  2M00373109+5743345  9.37956  57.7263  2M03244820+6300289  51.2009  63.008  2M03271166+6240211  51.7986  62.6725  2M03330010+6330443  53.2504  63.5123  2M03395727+6227241  54.9886  62.4567  2M06393827+2403560  99.9095  24.0656  2M05152962+2400147  78.8734  24.0041  2M03103113+4831002  47.6297  48.5167  2M03221451+4756591  50.5605  47.9498  2M05522651+4329557  88.1105  43.4988  2M20553607+5613011  313.9  56.217  2M21031081+5414127  315.795  54.2369  2M21084459+5442122  317.186  54.7034  2M21590597+4539010  329.775  45.6503  2M21554492+5414593  328.937  54.2498  2M21573025+5440529  329.376  54.6814  2M21594113+5351121  329.921  53.8534  2M22085910+5434192  332.246  54.572  2M18300408+0416050  277.517  4.26807  2M04113023+2255071  62.876  22.9187  2M06531594−0439506  103.316  −4.66407  View Large In Table A4, we list the spectroscopic binaries that were detected as outliers. Table A4. Spectroscopic binaries. APOGEE ID  RA (°)  Dec. (°)  2M14251536+3915337  216.314  39.2594  2M08115373+3212036  122.974  32.201  2M12274221+0002386  186.926  0.044058  2M05284223+4359528  82.176  43.998  2M05240837+2711064  81.0349  27.1851  2M03563567+7857072  59.1487  78.952  2M09314691+5618248  142.945  56.3069  2M13413548−1723167  205.398  −17.388  2M01193634+8435481  19.9014  84.5967  2M14542303+3122323  223.596  31.3756  2M09315645+3714213  142.985  37.2393  2M10280514+1735219  157.021  17.5894  2M11081296-1205110  167.054  −12.0864  2M21302403+1132483  322.6  11.5468  2M15002128+3645004  225.089  36.7501  2M18460678-0337057  281.528  −3.61827  2M19430973+2357587  295.791  23.9663  2M19225746+3824509  290.739  38.4141  2M21523747+3853140  328.156  38.8872  2M18054943−3059442  271.456  −30.9956  2M18075069−3116452  271.961  −31.2792  2M18081808−2553287  272.075  −25.8913  2M17561341−2921380  269.056  −29.3606  2M17360668−2710099  264.028  −27.1694  2M18103554−1811011  272.648  −18.1836  2M18192203−1411326  274.842  −14.1924  2M18192899−1452043  274.871  −14.8679  2M18280206−1217422  277.009  −12.2951  2M17345651−2048568  263.735  −20.8158  2M18001201−2631398  270.05  −26.5277  2M18041435−2455385  271.06  −24.9274  2M18165573−1852394  274.232  −18.8776  2M18040248−1805575  271.01  −18.0993  2M17464152−2713191  266.673  −27.222  2M17531813−2816161  268.326  −28.2711  2M18042203−2917298  271.092  −29.2916  2M17282574−2906578  262.107  −29.1161  2M17285197−2815064  262.217  −28.2518  2M18104783−2824046  272.699  −28.4013  2M19383737+4957227  294.656  49.9563  2M19454606+5113275  296.442  51.2243  2M19301580+4932086  292.566  49.5357  2M18544916+4512355  283.705  45.2099  2M19123630+4603326  288.151  46.0591  2M01593686+6533283  29.9036  65.5579  2M14370236+0928340  219.26  9.47612  2M15021575+2319460  225.566  23.3295  2M11254661+5217235  171.444  52.2899  2M11012916+1215329  165.372  12.2592  2M13405651+0031563  205.235  0.532321  2M18411589−1016542  280.316  −10.2817  2M19564877+4458058  299.203  44.9683  2M20034832+4536148  300.951  45.6041  2M19190180+4153127  289.758  41.8869  2M19561994+4120265  299.083  41.3407  2M13483079+1750445  207.128  17.8457  2M12115853+1425463  182.994  14.4295  2M12462044+1251325  191.585  12.8591  2M12505092+1324147  192.712  13.4041  2M14123798+5426481  213.158  54.4467  2M11542519+5554150  178.605  55.9042  2M11044917+4840467  166.205  48.6797  2M14232001+0541233  215.833  5.68982  2M16582628+0939165  254.61  9.65459  2M09242547−0650183  141.106  −6.83842  2M19400944+3832454  295.039  38.546  2M00065508+0154022  1.72953  1.90061  2M05502340+0420349  87.5975  4.34304  2M07054011+3812529  106.417  38.2147  2M07250686+2435451  111.279  24.5959  2M05345563−0601036  83.7318  −6.01768  2M05350392−0529033  83.7663  −5.48426  2M05360185−0517365  84.0077  −5.29349  2M05350138−0615175  83.7558  −6.25487  2M05351236−0543184  83.8015  −5.7218  2M05351561−0524030  83.8151  −5.40085  2M05351798−0604430  83.8249  −6.07862  2M05371161−0723239  84.2984  −7.38999  2M19383668+4723194  294.653  47.3887  2M18534305+0026394  283.429  0.444304  2M04135110+4938317  63.463  49.6422  2M03361242+4651208  54.0518  46.8558  2M17393731−2324309  264.905  −23.4086  2M17340500−2808243  263.521  −28.1401  2M18234612−1501159  275.942  −15.0211  2M17190649−2745172  259.777  −27.7548  2M17380171−2858281  264.507  −28.9745  2M17535762−2841520  268.49  −28.6978  2M17144370−2449231  258.682  −24.8231  2M17364991−2728343  264.208  −27.4762  2M19252567+4229371  291.357  42.4936  APOGEE ID  RA (°)  Dec. (°)  2M14251536+3915337  216.314  39.2594  2M08115373+3212036  122.974  32.201  2M12274221+0002386  186.926  0.044058  2M05284223+4359528  82.176  43.998  2M05240837+2711064  81.0349  27.1851  2M03563567+7857072  59.1487  78.952  2M09314691+5618248  142.945  56.3069  2M13413548−1723167  205.398  −17.388  2M01193634+8435481  19.9014  84.5967  2M14542303+3122323  223.596  31.3756  2M09315645+3714213  142.985  37.2393  2M10280514+1735219  157.021  17.5894  2M11081296-1205110  167.054  −12.0864  2M21302403+1132483  322.6  11.5468  2M15002128+3645004  225.089  36.7501  2M18460678-0337057  281.528  −3.61827  2M19430973+2357587  295.791  23.9663  2M19225746+3824509  290.739  38.4141  2M21523747+3853140  328.156  38.8872  2M18054943−3059442  271.456  −30.9956  2M18075069−3116452  271.961  −31.2792  2M18081808−2553287  272.075  −25.8913  2M17561341−2921380  269.056  −29.3606  2M17360668−2710099  264.028  −27.1694  2M18103554−1811011  272.648  −18.1836  2M18192203−1411326  274.842  −14.1924  2M18192899−1452043  274.871  −14.8679  2M18280206−1217422  277.009  −12.2951  2M17345651−2048568  263.735  −20.8158  2M18001201−2631398  270.05  −26.5277  2M18041435−2455385  271.06  −24.9274  2M18165573−1852394  274.232  −18.8776  2M18040248−1805575  271.01  −18.0993  2M17464152−2713191  266.673  −27.222  2M17531813−2816161  268.326  −28.2711  2M18042203−2917298  271.092  −29.2916  2M17282574−2906578  262.107  −29.1161  2M17285197−2815064  262.217  −28.2518  2M18104783−2824046  272.699  −28.4013  2M19383737+4957227  294.656  49.9563  2M19454606+5113275  296.442  51.2243  2M19301580+4932086  292.566  49.5357  2M18544916+4512355  283.705  45.2099  2M19123630+4603326  288.151  46.0591  2M01593686+6533283  29.9036  65.5579  2M14370236+0928340  219.26  9.47612  2M15021575+2319460  225.566  23.3295  2M11254661+5217235  171.444  52.2899  2M11012916+1215329  165.372  12.2592  2M13405651+0031563  205.235  0.532321  2M18411589−1016542  280.316  −10.2817  2M19564877+4458058  299.203  44.9683  2M20034832+4536148  300.951  45.6041  2M19190180+4153127  289.758  41.8869  2M19561994+4120265  299.083  41.3407  2M13483079+1750445  207.128  17.8457  2M12115853+1425463  182.994  14.4295  2M12462044+1251325  191.585  12.8591  2M12505092+1324147  192.712  13.4041  2M14123798+5426481  213.158  54.4467  2M11542519+5554150  178.605  55.9042  2M11044917+4840467  166.205  48.6797  2M14232001+0541233  215.833  5.68982  2M16582628+0939165  254.61  9.65459  2M09242547−0650183  141.106  −6.83842  2M19400944+3832454  295.039  38.546  2M00065508+0154022  1.72953  1.90061  2M05502340+0420349  87.5975  4.34304  2M07054011+3812529  106.417  38.2147  2M07250686+2435451  111.279  24.5959  2M05345563−0601036  83.7318  −6.01768  2M05350392−0529033  83.7663  −5.48426  2M05360185−0517365  84.0077  −5.29349  2M05350138−0615175  83.7558  −6.25487  2M05351236−0543184  83.8015  −5.7218  2M05351561−0524030  83.8151  −5.40085  2M05351798−0604430  83.8249  −6.07862  2M05371161−0723239  84.2984  −7.38999  2M19383668+4723194  294.653  47.3887  2M18534305+0026394  283.429  0.444304  2M04135110+4938317  63.463  49.6422  2M03361242+4651208  54.0518  46.8558  2M17393731−2324309  264.905  −23.4086  2M17340500−2808243  263.521  −28.1401  2M18234612−1501159  275.942  −15.0211  2M17190649−2745172  259.777  −27.7548  2M17380171−2858281  264.507  −28.9745  2M17535762−2841520  268.49  −28.6978  2M17144370−2449231  258.682  −24.8231  2M17364991−2728343  264.208  −27.4762  2M19252567+4229371  291.357  42.4936  View Large In Table A5, we list the spectroscopic binaries that were detected as outliers. Table A5. Fast rotators. APOGEE ID  RA (°)  Dec. (°)  2M21031344+0942207  315.806  9.70577  2M07365631+4517467  114.235  45.2963  2M07560603+2626563  119.025  26.449  2M17550303−2557141  268.763  −25.9539  2M18423451−0422454  280.644  −4.3793  2M11431652+0047511  175.819  0.797531  2M04131296+5546540  63.304  55.7817  2M03283689+7947391  52.1537  79.7942  2M16132421+5140269  243.351  51.6742  2M13553588+4436441  208.9  44.6123  2M18451898−0150567  281.329  −1.84909  2M19421896+2426209  295.579  24.4392  2M21131747+4843554  318.323  48.7321  2M20111813+2058271  302.826  20.9742  2M03464878+2304074  56.7033  23.0687  2M19142629+1202560  288.61  12.0489  2M20204714+3702309  305.196  37.0419  2M19014937+0520105  285.456  5.33626  2M03220356+5654161  50.5148  56.9045  2M17434496−2941008  265.937  −29.6836  2M18142425−1911037  273.601  −19.1844  2M18202527−1537239  275.105  −15.6233  2M18313707−1222341  277.904  −12.3761  2M19154842+4636261  288.952  46.6073  2M19544569+4041406  298.69  40.6946  2M20000263+4529265  300.011  45.4907  2M18543899+0012432  283.662  0.212021  2M19522028+2723553  298.085  27.3987  APOGEE ID  RA (°)  Dec. (°)  2M21031344+0942207  315.806  9.70577  2M07365631+4517467  114.235  45.2963  2M07560603+2626563  119.025  26.449  2M17550303−2557141  268.763  −25.9539  2M18423451−0422454  280.644  −4.3793  2M11431652+0047511  175.819  0.797531  2M04131296+5546540  63.304  55.7817  2M03283689+7947391  52.1537  79.7942  2M16132421+5140269  243.351  51.6742  2M13553588+4436441  208.9  44.6123  2M18451898−0150567  281.329  −1.84909  2M19421896+2426209  295.579  24.4392  2M21131747+4843554  318.323  48.7321  2M20111813+2058271  302.826  20.9742  2M03464878+2304074  56.7033  23.0687  2M19142629+1202560  288.61  12.0489  2M20204714+3702309  305.196  37.0419  2M19014937+0520105  285.456  5.33626  2M03220356+5654161  50.5148  56.9045  2M17434496−2941008  265.937  −29.6836  2M18142425−1911037  273.601  −19.1844  2M18202527−1537239  275.105  −15.6233  2M18313707−1222341  277.904  −12.3761  2M19154842+4636261  288.952  46.6073  2M19544569+4041406  298.69  40.6946  2M20000263+4529265  300.011  45.4907  2M18543899+0012432  283.662  0.212021  2M19522028+2723553  298.085  27.3987  View Large In Table A6, we list the objects with bad DR14 reductions. Table A6. Bad reductions. APOGEE ID  RA (°)  Dec. (°)  2M00354276+8619045  8.92818  86.3179  2M19315445+4813349  292.977  48.2264  2M19294950+4740246  292.456  47.6735  2M00250046+5503033  6.25194  55.0509  2M19205656+4846274  290.236  48.7743  2M19410822+4019319  295.284  40.3255  2M19325505+4746578  293.229  47.7827  2M19432504+2229419  295.854  22.495  2M06365780+0702069  99.2409  7.03526  2M19100818−0553311  287.534  −5.89197  2M07542422+3916064  118.601  39.2685  2M19193061+4842214  289.878  48.706  2M19341894+4800216  293.579  48.006  2M18315699−0100106  277.987  −1.00296  2M21223490+5110033  320.645  51.1676  2M14315024+5101159  217.959  51.0211  2M03292627+4656162  52.3595  46.9379  2M05322756+2658537  83.1149  26.9816  2M19130107−0549328  288.254  −5.82579  2M19411184+4013301  295.299  40.225  2M19455347+2412201  296.473  24.2056  2M14283924+4014496  217.164  40.2471  2M19570041+2059538  299.252  20.9983  2M19522176+1840186  298.091  18.6718  2M20464928+3411241  311.705  34.19  2M14273401+4014470  216.892  40.2464  2M20353553+5428403  308.898  54.4779  2M19343359+4823093  293.64  48.3859  2M03324489+4623388  53.1871  46.3941  2M19441693+4905154  296.071  49.0876  2M07014143+0449051  105.423  4.81809  2M21201614−0109393  320.067  −1.16092  2M08235914+0008354  125.996  0.143176  2M23583343+5635047  359.639  56.5847  2M23230618+5733020  350.776  57.5506  2M06381497+0557479  99.5624  5.96331  2M17470159−2849173  266.757  −28.8215  2M04424759+3825359  70.6983  38.4267  2M19095216+1120219  287.467  11.3394  2M17103385+3641103  257.641  36.6862  2M17192832+5804145  259.868  58.0707  2M07591385+4049311  119.808  40.8253  2M08013264+4307298  120.386  43.125  2M09095175+4254040  137.466  42.9011  2M09104765+4139238  137.699  41.6566  2M10265734+4149117  156.739  41.8199  2M10400281+4306255  160.012  43.1071  2M16011348+4149493  240.306  41.8304  2M16023049+3949503  240.627  39.8306  2M16034776+4051552  240.949  40.8653  2M16034893+4047314  240.954  40.7921  2M16042629+4030585  241.11  40.5163  2M16060267+4042385  241.511  40.7107  2M16062342+4023224  241.598  40.3896  2M16065762+4012407  241.74  40.2113  2M16103330+4146123  242.639  41.7701  2M16111200+4132006  242.8  41.5335  2M14250643+3912427  216.277  39.2119  2M14263122+3921276  216.63  39.3577  2M14264018+4018477  216.667  40.3133  2M14270892+4008013  216.787  40.1337  2M14285271+4015518  217.22  40.2644  2M06285236+0007407  97.2182  0.127981  2M13441054+2735078  206.044  27.5855  2M16280255−1306104  247.011  −13.1029  2M17181861+4206399  259.578  42.1111  2M09082892+3618428  137.121  36.3119  2M13044971+7301298  196.207  73.025  2M09294773+5544429  142.449  55.7453  2M12242677+2534571  186.112  25.5826  2M12283815+2613370  187.159  26.227  2M12284457+2553575  187.186  25.8993  2M10265302+1713099  156.721  17.2194  2M10282637+1545209  157.11  15.7558  2M21310488+1250496  322.77  12.8471  2M13500810+4233262  207.534  42.5573  2M10521368+0101300  163.057  1.02502  2M21103095+4741321  317.629  47.6923  2M18312899−0138055  277.871  −1.63487  2M18350820+0002348  278.784  0.043008  2M20510547+5125023  312.773  51.4173  2M20535239+4932004  313.468  49.5334  2M21302584+4452299  322.608  44.875  2M20321595+5337112  308.066  53.6198  2M07115139+0539169  107.964  5.65472  2M18043735+0155085  271.156  1.91904  2M20412525+3317111  310.355  33.2864  2M19532259+0424013  298.344  4.40037  2M04291231+3515567  67.3013  35.2658  2M16553254−2134100  253.886  −21.5695  2M17564183−2803554  269.174  −28.0654  2M19281906+4915086  292.079  49.2524  2M19331420+4841507  293.309  48.6974  2M19343984+4809524  293.666  48.1646  2M19094607+3747391  287.442  37.7942  2M23483899+6452355  357.162  64.8765  2M04060214+4655320  61.5089  46.9256  2M22190955−0133473  334.79  −1.56315  2M22472985+0553172  341.874  5.88812  2M14442821+4511096  221.118  45.186  2M14493515+4634280  222.396  46.5745  2M14140537+5438148  213.522  54.6375  2M23240792+5732077  351.033  57.5355  2M05060092+3556109  76.5038  35.9364  2M06202991+0723133  95.1246  7.38705  2M03353783+3140491  53.9077  31.6803  2M05350478−0443546  83.7699  −4.73184  2M21425212+6955149  325.717  69.9208  2M00474266+0351290  11.9278  3.85806  2M02275302−0855544  36.971  −8.9318  2M02310705−0758192  37.7794  −7.97201  2M02325195−0806163  38.2165  −8.10455  2M02332095−0903458  38.3373  −9.06273  2M14484064−0706253  222.169  −7.10704  2M14362130+5733384  219.089  57.5607  2M06391017+0518525  99.7924  5.3146  2M09235450+2753539  140.977  27.8983  2M09260229+2839009  141.51  28.6503  2M00525338+3832558  13.2224  38.5488  2M15122530+6658305  228.105  66.9752  2M05495923+4136264  87.4968  41.6073  2M06232278−0441150  95.845  −4.68751  2M11540771+1810106  178.532  18.1696  2M15044648+2224548  226.194  22.4152  APOGEE ID  RA (°)  Dec. (°)  2M00354276+8619045  8.92818  86.3179  2M19315445+4813349  292.977  48.2264  2M19294950+4740246  292.456  47.6735  2M00250046+5503033  6.25194  55.0509  2M19205656+4846274  290.236  48.7743  2M19410822+4019319  295.284  40.3255  2M19325505+4746578  293.229  47.7827  2M19432504+2229419  295.854  22.495  2M06365780+0702069  99.2409  7.03526  2M19100818−0553311  287.534  −5.89197  2M07542422+3916064  118.601  39.2685  2M19193061+4842214  289.878  48.706  2M19341894+4800216  293.579  48.006  2M18315699−0100106  277.987  −1.00296  2M21223490+5110033  320.645  51.1676  2M14315024+5101159  217.959  51.0211  2M03292627+4656162  52.3595  46.9379  2M05322756+2658537  83.1149  26.9816  2M19130107−0549328  288.254  −5.82579  2M19411184+4013301  295.299  40.225  2M19455347+2412201  296.473  24.2056  2M14283924+4014496  217.164  40.2471  2M19570041+2059538  299.252  20.9983  2M19522176+1840186  298.091  18.6718  2M20464928+3411241  311.705  34.19  2M14273401+4014470  216.892  40.2464  2M20353553+5428403  308.898  54.4779  2M19343359+4823093  293.64  48.3859  2M03324489+4623388  53.1871  46.3941  2M19441693+4905154  296.071  49.0876  2M07014143+0449051  105.423  4.81809  2M21201614−0109393  320.067  −1.16092  2M08235914+0008354  125.996  0.143176  2M23583343+5635047  359.639  56.5847  2M23230618+5733020  350.776  57.5506  2M06381497+0557479  99.5624  5.96331  2M17470159−2849173  266.757  −28.8215  2M04424759+3825359  70.6983  38.4267  2M19095216+1120219  287.467  11.3394  2M17103385+3641103  257.641  36.6862  2M17192832+5804145  259.868  58.0707  2M07591385+4049311  119.808  40.8253  2M08013264+4307298  120.386  43.125  2M09095175+4254040  137.466  42.9011  2M09104765+4139238  137.699  41.6566  2M10265734+4149117  156.739  41.8199  2M10400281+4306255  160.012  43.1071  2M16011348+4149493  240.306  41.8304  2M16023049+3949503  240.627  39.8306  2M16034776+4051552  240.949  40.8653  2M16034893+4047314  240.954  40.7921  2M16042629+4030585  241.11  40.5163  2M16060267+4042385  241.511  40.7107  2M16062342+4023224  241.598  40.3896  2M16065762+4012407  241.74  40.2113  2M16103330+4146123  242.639  41.7701  2M16111200+4132006  242.8  41.5335  2M14250643+3912427  216.277  39.2119  2M14263122+3921276  216.63  39.3577  2M14264018+4018477  216.667  40.3133  2M14270892+4008013  216.787  40.1337  2M14285271+4015518  217.22  40.2644  2M06285236+0007407  97.2182  0.127981  2M13441054+2735078  206.044  27.5855  2M16280255−1306104  247.011  −13.1029  2M17181861+4206399  259.578  42.1111  2M09082892+3618428  137.121  36.3119  2M13044971+7301298  196.207  73.025  2M09294773+5544429  142.449  55.7453  2M12242677+2534571  186.112  25.5826  2M12283815+2613370  187.159  26.227  2M12284457+2553575  187.186  25.8993  2M10265302+1713099  156.721  17.2194  2M10282637+1545209  157.11  15.7558  2M21310488+1250496  322.77  12.8471  2M13500810+4233262  207.534  42.5573  2M10521368+0101300  163.057  1.02502  2M21103095+4741321  317.629  47.6923  2M18312899−0138055  277.871  −1.63487  2M18350820+0002348  278.784  0.043008  2M20510547+5125023  312.773  51.4173  2M20535239+4932004  313.468  49.5334  2M21302584+4452299  322.608  44.875  2M20321595+5337112  308.066  53.6198  2M07115139+0539169  107.964  5.65472  2M18043735+0155085  271.156  1.91904  2M20412525+3317111  310.355  33.2864  2M19532259+0424013  298.344  4.40037  2M04291231+3515567  67.3013  35.2658  2M16553254−2134100  253.886  −21.5695  2M17564183−2803554  269.174  −28.0654  2M19281906+4915086  292.079  49.2524  2M19331420+4841507  293.309  48.6974  2M19343984+4809524  293.666  48.1646  2M19094607+3747391  287.442  37.7942  2M23483899+6452355  357.162  64.8765  2M04060214+4655320  61.5089  46.9256  2M22190955−0133473  334.79  −1.56315  2M22472985+0553172  341.874  5.88812  2M14442821+4511096  221.118  45.186  2M14493515+4634280  222.396  46.5745  2M14140537+5438148  213.522  54.6375  2M23240792+5732077  351.033  57.5355  2M05060092+3556109  76.5038  35.9364  2M06202991+0723133  95.1246  7.38705  2M03353783+3140491  53.9077  31.6803  2M05350478−0443546  83.7699  −4.73184  2M21425212+6955149  325.717  69.9208  2M00474266+0351290  11.9278  3.85806  2M02275302−0855544  36.971  −8.9318  2M02310705−0758192  37.7794  −7.97201  2M02325195−0806163  38.2165  −8.10455  2M02332095−0903458  38.3373  −9.06273  2M14484064−0706253  222.169  −7.10704  2M14362130+5733384  219.089  57.5607  2M06391017+0518525  99.7924  5.3146  2M09235450+2753539  140.977  27.8983  2M09260229+2839009  141.51  28.6503  2M00525338+3832558  13.2224  38.5488  2M15122530+6658305  228.105  66.9752  2M05495923+4136264  87.4968  41.6073  2M06232278−0441150  95.845  −4.68751  2M11540771+1810106  178.532  18.1696  2M15044648+2224548  226.194  22.4152  View Large © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Notices of the Royal Astronomical Society Oxford University Press

Detecting outliers and learning complex structures with large spectroscopic surveys – a case study with APOGEE stars

Loading next page...
 
/lp/ou_press/detecting-outliers-and-learning-complex-structures-with-large-ettUudfcYX
Publisher
The Royal Astronomical Society
Copyright
© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
ISSN
0035-8711
eISSN
1365-2966
D.O.I.
10.1093/mnras/sty348
Publisher site
See Article on Publisher Site

Abstract

Abstract In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data. methods: data analysis, stars: general, stars: peculiar 1 INTRODUCTION Extracting and analysing information from ongoing and future astronomical surveys, with their increasing size and complexity, requires astronomers to take advantage of the tools developed in the (also rapidly growing) fields of data science and machine learning (ML). Most commonly in astronomy, these methods enable detection or classification of specified objects using supervised ML algorithms, while unsupervised ML is used to search for correlations or clusters in high dimensional data. Recent examples for such work are Bloom et al. (2012) – identification and classification of transits and variable stars using imaging, Meusinger et al. (2012) – outlier detection with quasar spectra, Masci et al. (2014) – classification of periodic variable stars using photometric time series, Baron et al. (2015) – clustering diffuse interstellar band lines based on their pairwise correlation, Miller et al. (2017) – Star-Galaxy classification based on imaging. A review of data science applications in astronomy can be found in Ball & Brunner (2010). In this work, we focus on unsupervised exploration of a data set based on a similarity matrix, containing a pair-wise similarity measure between every two objects (the simplest possible measure being the Euclidean distance between the features of two objects). We show that such a similarity matrix (or its inverse, the distance matrix) is a powerful tool for exploring a data set in a data-driven way. We calculate an unsupervised random forest (RF) based similarity measure for stellar spectra and show that, without any additional input other than the spectra themselves, the similarity matrix traces physical properties such as metallicity, effective temperature and surface gravity. This allows us to visualize the complex structure of the data set, to query for similar objects based on their spectra alone, to put an object in the context of the general population, to scan the data set for different object types, and to detect outliers. These possibilities are only partly available with traditional representation of an object in an astronomical data base, i.e. by its fit parameters. Fitting a model requires making assumptions about the object. This can work well for a large fraction of the data, but usually cannot account for all the objects nor all of the features. In data sets composed of astronomical spectra, the model fitting is usually based on spectral templates, that do not cover the entire range of parameters available in the data set. Furthermore, templates are usually not available for rare or unexpected objects. This leaves a fraction of the objects, even if well understood, not well fitted, and impossible to query using the data base. Generative models have recently gained popularity within the astronomical community and outside of it, as they solve some of the issues raised above. Generative models are generated from the data set, with few to no assumption about the data structure and distribution of information content. These models, which are purely data-driven, have been shown to generalize well, and describe even the most extreme objects in the sample without the need for dedicated treatment. An example of generative models is generative adversarial neural networks (GANs), for a recent use in astrophysics see Schawinski et al. (2017). In this work, we show that the unsupervised RF algorithm can be viewed as a generative model, as it grasps complex features in the data set, and is able to describe the most extreme objects in the sample in the same context as the common ones. Perhaps the most intriguing usage of a similarity matrix is outlier detection. Outliers in a data set can have different origins and interpretations. Some are measurement or data processing errors, and others are objects not expected to be in the data set, extreme and rare objects, and most importantly, unknown unknowns – objects we did not know we should be looking for. In addition, in astronomy, rare objects could actually be important and common evolutionary phases that are short lived, and therefore challenging to observe. It is worth noting that finding the mundane outliers is still useful in order to clean the data set from erroneous and unwanted objects, to allow for a better analysis of the rest of the sample. Outlier detection algorithms can be divided into different types: (i) Distance based algorithms, which we use in this work, relying on a (case specific) definition of a pair-wise distance between the objects, (ii) Probabilistic algorithms, based on estimating the probability density function of the data, (iii) Domain based algorithms, which create boundaries in feature space, (iv) Reconstruction based algorithms, which model the data and calculate the reconstruction error as a measure for novelty, and (v) Information-theoretic algorithms, which use the information content of the data (for example by computing the entropy of the data), and measure how specific objects in the data set change this value. For a review, see Pimentel et al. (2014). We use a distance based algorithm as it allows us to explore the data in additional ways, as discussed above. One thing to note is that for a large and complex enough data set it is likely that there is not a single outlier detection algorithm that is best, i.e. one algorithm that detects all the interesting outliers. In general, different algorithms could be sensitive to different types of outliers. An obvious test for such an algorithm is whether it detects the expected outliers, if it does then it could be worthwhile to investigate all the detected outliers. But even then there is no guarantee that a different algorithm would not detect additional interesting objects. In this work, we expand the outlier detection algorithm presented in Baron & Poznanski (2017)1 and apply it to infrared stellar spectra. The core of the algorithm is calculating a distance matrix of the objects in the sample. This distance is based on RF dissimilarity. For RF see Breiman et al. (1984); Breiman (2001), for RF dissimilarity see Breiman & Cutler (2003); Shi & Horvath (2006). There are many possible choices for a similarity measure, a simple example being the euclidian distance between the features of the objects. See Yang (2006) for a survey of distance metric learning. It is known [see Yang (2006), and references therein] that a good choice of distance metric can improve the accuracy of K-nearest-neighbour classification (a common application of a distance metric), over simple euclidian distances. Similarly to outlier detection algorithms, there is no best distance metric, even for a specific data set. As there are many possible usages for a distance metric, it is even less clear how such best distance metric would be defined. An intuitive reason to use RF dissimilarity is that, as described below, it is sensitive to the correlation between different features. This is is often of importance in spectra. For instance, line ratios are usually of more interest then the strength of a single line. A euclidian distance metric will be more sensitive to strength of single lines. See Garcia-Dias et al. (2018) for an application of an euclidian distance metric in a clustering algorithm with APOGEE spectra. Baron & Poznanski (2017) applied this algorithm to find outliers in galaxy spectra from Sloan Digital Sky Survey (SDSS; Eisenstein et al. 2011) and used the distance matrix to detect outliers. They found spectra showing various rare phenomena such as supernovae, galaxy–galaxy gravitational lenses, and double peaked emission-lines, as well as the first reported evidence for active galactic nucleus (AGN)-driven outflows, traced by ionized gas, in post starburst E+A galaxies. The last discovery is discussed in Baron et al. (2017). The algorithm was applied to galaxy spectra using the flux values at every wavelength as features for the RF (i.e. without generating user defined features). Here, we do the same with stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE, Majewski, APOGEE Team & APOGEE-2 Team 2016), which is part of the SDSS-III, and explore additional applications of the distance matrix produced by the algorithm. We visualize the distance matrix using the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm (van der Maaten & Hinton 2008), find objects which are similar to objects of interest, and find the most similar objects in the data set (that is – spectroscopic twins). This paper is organized as follows. Section 2 describes the APOGEE data set we use in this work. In Section 3, we use t-SNE to visualize the distance matrix produced by our algorithm, and show that it traces stellar parameters. We use the distance matrix to find groups of similar objects, and spectroscopic twins. In Section 4, we discuss ways to select and classify outliers efficiently. In Section 5, we present the classification of the outliers we detected. We summarize in Section 6. 2 APOGEE SPECTRA The 14th SDSS data release (DR14; Abolfathi et al. 2017) contains the first data release for the APOGEE-2 survey. The APOGEE-2 survey consists of high resolution (R ∼ 22 500), high signal to noise ratio (S/N; typically S/N > 100), infrared H-band (1.51–1.70 μm) spectra for ∼263 000 different stars. The APOGEE-2 main survey spans all galactic environments (bulge, disc, and halo) and is composed mainly of red giant stars. The main survey targets were chosen using a cut on the H-band magnitude, gravity-sensitive optical photometry, and dereddened (J − Ks)0 colour limits. The colour limit and optical photometry criteria are intended to separate red giants from main sequence dwarfs. The APOGEE-2 data set contains ∼32 000 non-main survey targets, including ∼13 300 ancillary targets, and ∼27 000 hot stars used for telluric correction. More details on the target selection in APOGEE-2 are in Zasowski et al. (2017). A large fraction of the work done with APOGEE data is devoted to investigating the Milky Way structure and evolution using chemical abundances and radial velocities (RVs) derived from the spectra; for examples, see Frinchaboy et al. (2013); Nidever et al. (2014); Bovy et al. (2014); Ness et al. (2015); Chiappini et al. (2015); Hayden et al. (2015). We note that APOGEE spectra are rich with information, and a single spectrum can contain hundreds of absorption lines. The input to our algorithm is the pseudo-continuum normalized (PCN) spectrum. The PCN procedure is done with the APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP; García Pérez et al. 2016) in order to remove variations of spectral shape arising from interstellar reddening, errors in relative fluxing, detector response, and broad-band atmospheric absorption. The APOGEE spectra contain two gaps in wavelength. Our preprocessing stage consists of removing flux values in these gaps (these values are set to zero in the original PCN spectrum), as well as interpolating the spectra to the same wavelength grid. This leaves us with 7514 flux values per object, which are the features used by the outlier detection algorithm. Applying our algorithm to the APOGEE-2 spectra from DR14, it became clear that many objects have faulty PCN spectra (these objects are discussed in Section 5). Our algorithm naturally classifies these objects as outliers, making it harder to find the more interesting outliers. For this reason, since DR13 does not suffer from this contamination, we apply the algorithm to DR13 data as well. DR13 contains spectra for 163 000 stars, 25 000 of which are non-main survey. DR13 contains results from APOGEE-1, for which the target selection is somewhat different, and is described in Zasowski et al. (2013). Unless otherwise stated the results presented in this paper refer to DR14, APOGEE-2 (which we will refer to as APOGEE) data. We use only objects with S/N > 100, of which there are 193 556 in DR14 (107 390 in DR13). The input data size is therefore the product of the number of objects by the number of features (wavelengths). The reason for not using low S/N objects is that when included, objects with spectra dominated by noise are detected as outliers. We note that for the high S/N objects we used, the weirdness score and the S/N are not correlated. 3 EXPLORING THE APOGEE DATA SET USING A DISTANCE MATRIX Using our distance matrix to find physically interesting outliers and study the structure of the data set requires it to retain the complex information that we see in each object in the sample, which is a non-trivial task. In this section, we explore what type of information our distance matrix contains. Baron & Poznanski (2017) have seen some hints that the RF distance matrix contains a wealth of complex spectral information aggregated to a single number, the pair-wise distance, here to explore that question using visualization and dimensionality reduction tools. 3.1 Random forest dissimilarity Briefly, the distance is calculated by the following procedure. First, synthetic data are created with the same marginal distributions as the original data in every feature, but stripped of the correlation between different features (the features in our application are the flux values at each wavelength of the spectra, as described below). Having two types of objects, one real and one synthetic, an RF classifier is trained to separate between the two. In the process of separating the synthetic objects with un-correlated features from the real ones, the RF learns to recognize correlations in the spectra of real objects. The RF is composed of a large number of classification trees, each tree is trained to separate real and synthetic objects using a subset of the data (the ‘Random’ in’RF’ is referring to the randomness in which a subset of the data is selected for each tree, see Breiman et al. (1984); Breiman (2001) for details). Having a large number of trees, the similarity S between two objects (objects in the original data set, i.e. real objects) is then calculated by counting the number of trees in which the two objects ended up on the same leaf (a leaf being a tree node with no children nodes), and dividing by the number of trees. This is done only for the trees in which both objects are classified as real. We define the distance matrix to be D = 1 − S. Using the distance matrix, we can calculate a ‘weirdness score’ for every object, defined to be the average distance to all other objects. Below we refer to this weirdness score as Wall. See Baron & Poznanski (2017) for a detailed description of the algorithm. To build the distance matrix, we use the scikit-learn implementation of RF. The number of trees we used is 5000. We note that this number was necessary to reach convergence, i.e. increasing this number further does not alter the results. Every 200 trees are built using a random subset of 10 000 objects. 3.2 The t-SNE algorithm t-SNE is a dimensionality reduction algorithm that is particularly well suited for the visualization of high-dimensional data sets. We use t-SNE to visualize our distance matrix. A-priori, these distances could define a space with almost as many dimensions as objects, i.e. tens of thousand of dimensions. Obviously, since many stars are quite similar, and their spectra are defined by a few physical parameters, the minimal spanning space might be smaller. By using t-SNE, we can examine the structure of our sample projected into 2D. We use our distance matrix as input to the t-SNE algorithm and in return get a 2D map of the objects in our data set. In this map, nearby objects have a small pair-wise distance, and distant objects have a large pair-wise distance. The two t-SNE dimensions have no physical interpretation. Since the dimensionality in greatly reduced in the process, this is approximate, and breaks for large distances. That is, the map does not show the relative pair-wise distance between ‘far away’ and ‘very far away’ objects. The map does preserve small scale structure. The general idea of the t-SNE algorithm is quite simple – trying to preserve the distances of each object to its nearest neighbours (the number of which is determined by the perplexity parameter), while forcing the distances to reside on a lower dimensional plane, in our case 2D. There is usually no single best t-SNE map. Maps calculated with different numbers of nearest neighbours can provide the user with different information about the data set. For example, a map calculated with 10 000 nearest neighbours is not likely to show a cluster that contains 100 objects, while a map with 100 nearest neighbours is. Other free parameters in t-SNE are of computational nature, and control speed versus accuracy (accuracy of approximations done in different calculations inside the algorithm). A bad choice of parameters is usually manifested by a large fraction of the objects distributed randomly on the map. We consider a map in which all or almost all of the objects are located in structures to be a good map. Once we have that we can change the perplexity to determine the ‘scale’ in which the objects are clustered. A guide for effective use of t-SNE is available in Wattenberg, Viégas & Johnson (2016). We use the scikit-learn (Pedregosa et al. 2011) implementation of t-SNE. We note that to get informative maps we had to significantly increase the learning rate parameter (in the t-SNE map shown below it was set to 40 000) from its default value of 1000. The perplexity we used was 2000. Both of these parameters required adjustment when changing the number of objects in the distance matrix. Building the map took about 3 d of computation on a machine with 32 cores and 1TB of RAM. When using the current version of scikit-learn (0.17), t-SNE is using memory of about eight times the size of the distance matrix. The memory usage will be significantly reduced in future scikit-learn versions. We used the development version of t-SNE, which will be included in scikit-learn 0.19. With this version the memory usage was reduced by roughly a factor of 4, depending on the perplexity. 3.3 A t-SNE map of the APOGEE data set We apply the t-SNE algorithm to our RF dissimilarity distance matrix. The map produced can be especially informative when using different object attributes to colour the points. In Fig. 1, we use the following for colour: Teff, highlight of M-type stars, metallicity, and log(g), based on the ASPCAP fit. Most of the objects lie in a right-hand side, mainly vertical, component of the map. In this part, we see that the stars are sequenced by their surface gravity, where giants are located at the top and dwarfs at the bottom, as well as their effective temperature for which we get two separate sequences, one for dwarfs at the bottom of the map and one for giants at the top of the map. We also see an horizontal sequence that follows the metallicity, high metallicity on the right-hand side. On the left-hand side of the map, we have the hotter stars in the APOGEE sample, including the stars used for telluric calibration. The very low metallicity stars are located near these telluric objects, both having mainly featureless spectra. Figure 1. View largeDownload slide t-SNE map of our distance matrix. Each point on the map represents a star, where spectrally similar objects cluster on small scales. The axes do not have any physical significance. In the different panels, different coloring schemes are presented. Panel (a): effective temperature, panel (b): surface gravity, panel (c): metallicity, and panel (d): highlighted M-type stars. The values used for the different coloring are taken from ASPCAP. Stars with no available value for a parameter do not appear on the map. For example, many dwarf stars do not have log g values, so the clusters containing dwarfs disappear from the log g map. The complex structure of the sample is apparent. In panels (e) and (f), we colour the map by the weirdness score. Wall is in panel (e), and W250 is in panel (f). We see that when using W250, low Teff stars no longer dominate the high weirdness score population, and we get a more diverse outlier population that is spread on the t-SNE map. Figure 1. View largeDownload slide t-SNE map of our distance matrix. Each point on the map represents a star, where spectrally similar objects cluster on small scales. The axes do not have any physical significance. In the different panels, different coloring schemes are presented. Panel (a): effective temperature, panel (b): surface gravity, panel (c): metallicity, and panel (d): highlighted M-type stars. The values used for the different coloring are taken from ASPCAP. Stars with no available value for a parameter do not appear on the map. For example, many dwarf stars do not have log g values, so the clusters containing dwarfs disappear from the log g map. The complex structure of the sample is apparent. In panels (e) and (f), we colour the map by the weirdness score. Wall is in panel (e), and W250 is in panel (f). We see that when using W250, low Teff stars no longer dominate the high weirdness score population, and we get a more diverse outlier population that is spread on the t-SNE map. In panel 1d, we see that some M-type stars are located far from the rest. We manually inspect these objects as an example to see if this is due to the algorithm mis-locating a few objects, or if these objects are really different from the rest of their respective groups. We find that in this case the objects really have different looking spectra, with poor ASPCAP fitting. For example, some of these misplaced-M-type stars turn out to be B-type emission line stars (Be stars). From the t-SNE maps, we learn that our distance matrix is capable of aggregating non-trivial information about the objects in the sample. Fig. 1 shows that the distance matrix hold information about various physical properties, namely the figure is showing sequences in the effective temperature, surface gravity, and metallicity. These properties, in addition to the chemical abundances, affect the spectral features in non-trivial and partly degenerate ways, which we see are captured in the distance matrix. The APOGEE pipeline derives the stellar parameters by means of best-fitting templates. We see that some of the stars in the sample do not have derived parameter values. This is usually because these stars are extreme, at least with respect to the rest of the sample, and their stellar parameters fall outside the grid of spectral templates used by the pipeline. One can use the distance matrix to find these objects. As seen on the t-SNE map, the algorithm places the extreme objects next to the less extreme ones in a continuous sequence. In this sense, we say that the similarity measure could be viewed as a generative model of the data. Seeing here that globally the distance matrix captures the structure of the data set, in the next two subsections we see that it could also be used to investigate the data set at a ‘smaller scale’ – by looking at the most similar objects. 3.4 Object retrieval We can use the distance matrix to query the data set for similar objects based on their spectra alone. We use the example of carbon rich stars to show that the algorithm can find objects that were not possible to find using their ASPCAP fit parameters. Carbon-rich stars have atmospheres with over-abundance of carbon compared to oxygen. In this case, the excess carbon (i.e. carbon that is not tied in CO) will allow CN (and other carbon molecules) to form. In regular stars, there will be excess oxygen, that will form OH. In Fig. 2, we show a t-SNE map colored by the carbon to oxygen abundance ratio, from ASPCAP. Figure 2. View largeDownload slide A t-SNE map coloured by the carbon to oxygen abundance ratio, from ASPCAP. We focus on a cluster of carbon rich stars. We see that, according to a sequence visibly detectable on the map, objects at the high end of the sequence are not fitted by ASPCAP. Detecting these objects is possible using the similarity matrix. Figure 2. View largeDownload slide A t-SNE map coloured by the carbon to oxygen abundance ratio, from ASPCAP. We focus on a cluster of carbon rich stars. We see that, according to a sequence visibly detectable on the map, objects at the high end of the sequence are not fitted by ASPCAP. Detecting these objects is possible using the similarity matrix. Focusing on a cluster of carbon rich stars, we see that the objects are sequenced on the map according the C/O abundance ratio. Most importantly we see that a number of objects without pipeline abundance value are located at the top of the sequence. We suggest that the pipeline is not able to fit these objects due to them having extreme abundances, and due to the difficulty of abundance analysis of spectra with very strong molecular lines. We manually inspect the 51 objects with no ASPCAP value shown in Fig. 2 and see that they all show strong CN and weak OH, typical for carbon rich stars. Moreover, all of the 35 objects that have SIMBAD (Wenger et al. 2000) entry are classified as carbon stars, making the 15 objects without SIMBAD entry carbon star candidates. We note that many of these objects were observed as part of the APOGEE-2 AGB stars ancillary program, see Zasowski et al. (2017). This in an example of a query for objects based on their spectra, instead of on their fit parameters. This is of importance for objects with bad or non-existent fit parameters, that would otherwise be lost in the data set. In a large enough data set, it is very likely such objects will exist, these can be extreme cases of known phenomenon outside the range of the model, rare objects outside the scope of the model, or other types of outliers in the data set. 3.5 Spectroscopic twins The distance matrix produced by the algorithm can be used for finding objects with spectra similar to each other, objects sometimes referred to as spectroscopic twins. This is trivially achieved by sorting the distance matrix. One use of spectroscopic twins is measuring distances (Jofré et al. 2015). Twin stars will have the same luminosity, and if we know the distance to one of the stars (e.g. using parallax), we can calculate the distance to the other by comparing observed magnitudes. Jofré et al. (2015) looked for spectroscopic twins among 536 FGK stars, and detected 175 pairs with spectra indistinguishable within the errors. As we work with a rather homogeneous sample of ∼105 stars, we expect a large fraction of (multiple) twins. Example spectra for spectroscopic twins are shown in A1(b). It can be seen that the pairs have virtually identical spectra. Note that in the top example in Fig. A1(b) one of the spectra lacks an ASPCAP fit, preventing identification of a twin via these parameters. The middle pair have very similar parameters, while the bottom twins show more significantly different parameters. Our method finds them all, irrespectively. One thing we note is that this method for finding twins works well for the common object types (common in terms of representation in the data set), but might be less so for underrepresented types of objects (in which case we still get similar spectra, but not identical). The reason being that our unsupervised RF uses more extensively the features that are important for regular objects, and as a result it can separate objects based on subtle differences in these features. For other features that might not be important to most of the objects (for example, hydrogen absorption or emission), the RF uses cruder cuts to separate objects. As a test for the selection of twin objects, we look at the angular separation of stars with similar spectra. APOGEE stars living in the same environment are more likely to have similar physical properties, and thus similar spectra. We expect that pairs of stars we detect as having similar spectra will have higher probability to be located near each other compared with random pairs of stars. We note that there is an observational effect playing a role here – the APOGEE sample is not uniform in different parts of the galaxy (for example, dwarfs from the galactic bulge are too faint for APOGEE and are not observed). In Fig. 3, we show the distribution of the angular separation of each star in our sample with its nearest neighbour (as defined by our distance matrix), compared to random pairs of stars. Clearly, the twins we find are physically associated. We release our entire distance matrix, and we will update it with future data releases, allowing others to use the twins for further study. We note that our methodology does not allow one to compare well objects with different S/N, nor does it give a statistically meaningful similarity measure. However, it bypasses the difficulties of comparing spectra in data or model space, while producing very robust results. An additional example for using a distance metric to find similar objects is found in Jofré et al. (2017), who looked for nearest neighbours on a t-SNE map of RAVE stars in search for spectroscopic twins. To build the t-SNE map euclidian distance were used. Figure 3. View largeDownload slide A comparison between angular separation of pairs of stars detected as having similar spectra, and random pairs of stars. The similar spectra stars shown here are each star in our sample and its nearest neighbour according to our distance matrix. Figure 3. View largeDownload slide A comparison between angular separation of pairs of stars detected as having similar spectra, and random pairs of stars. The similar spectra stars shown here are each star in our sample and its nearest neighbour according to our distance matrix. 4 EFFICIENT OUTLIER DETECTION An important usage of the distance matrix is outlier detection. For this purpose, we calculate a weirdness score for each object in the sample. This weirdness score is calculated by summing over the distance matrix. In this section, we use the t-SNE visualization in order to understand the properties of this weirdness score. We present a new, local, definition of a weirdness score. We use this local weirdness score for APOGEE stars, and find it is more suitable for detecting outliers. We can use the t-SNE map in order learn about the weirdness score properties. In Fig. 1(e), we colour the t-SNE map by the weirdness score. The central, low Wall part of the t-SNE map contains about half of the objects in the sample. These objects are G and K giant stars, with weak molecular features in their spectra, but with prominent metallic features. They comprise one large group of objects with similar spectra. Below we refer to these objects as the main group. Example spectra for such objects are presented in Fig. A1(a). For each object in the figure, we present the percentile of the object's weirdness score, i.e. the percent of the objects with lower weirdness score. In order to better understand the properties of Wall, we examine its distribution in Fig. 4. The distribution decreases smoothly to high weirdness except for two bumps. We interpret the bumps as clusters of stars in our similarity space. One bump consists partially of low-temperature stars, and the other is due to stars with weak or non-existent absorption lines – metal poor stars and telluric calibration targets. The bumps in the distribution of Wall are due to the fact that there is one dominant cluster of objects in the data set, and the objects in smaller clusters receive a weirdness score based on how different they are from objects in the main cluster. These results might be useful to detect clusters, or to clean up the data set from objects outside the main group, but in order to find small classes of interesting outliers, we need a better outlier definition. Figure 4. View largeDownload slide Wall distribution for all objects in the sample. The two bumps in the distribution are composed mostly of objects with no ASPCAP fit parameters. Inspecting the spectra of these objects, we see that one bump contains low Teff stars, while the other contains low metallicity and hot telluric calibration stars (i.e. stars with weak or non-existent absorption lines). Figure 4. View largeDownload slide Wall distribution for all objects in the sample. The two bumps in the distribution are composed mostly of objects with no ASPCAP fit parameters. Inspecting the spectra of these objects, we see that one bump contains low Teff stars, while the other contains low metallicity and hot telluric calibration stars (i.e. stars with weak or non-existent absorption lines). To address the issue described above, we introduce the ‘nearest neighbours weirdness score’, a modification to the algorithm that produces ‘better outliers’ for the APOGEE data set. When looking for better outliers, we wish to get several different types of objects detected as outliers, in contrast to a weirdness score that strongly correlates to a single attribute (e.g. the effective temperature). In addition, we expect to be able to detect known outliers such as binaries and bad spectra. Instead of defining outliers based on their average distance to the entire sample, we use a more local measure, and for every object we calculate distances to its nearest neighbours. This measure of unusualness is used for distance-based outlier detection in other fields (Knorr & Ng 1999; Knorr, Ng & Tucakov 2000). The resulting weirdness score distribution is shown in Fig. 5. We can see that the bumps in the weirdness score distribution go away for a small enough number of nearest neighbours. When choosing the number of nearest neighbours to use in the weirdness score calculation, one can check at what point the weirdness score distribution does not contain bumps. Figure 5. View largeDownload slide Weirdness score distribution for different numbers of nearest neighbours included in the calculation. Figure 5. View largeDownload slide Weirdness score distribution for different numbers of nearest neighbours included in the calculation. A t-SNE map with the 250 nearest neighbours weirdness score (W250) is shown in Fig. 1(f). Clearly, there is a group of stars that have persistently high (percentile) weirdness score for any number of nearest neighbours used. On the other hand, the high Teff stars no longer have high weirdness score for small numbers of nearest neighbours. This results in various other groups of stars receiving higher percentile weirdness score. An open question regarding many outlier detection algorithms is setting a threshold on the weirdness score, i.e. determining above which weirdness score we mark an object as an ‘outlier’ and inspect it further. The t-SNE map could be of help here too: looking at the W250 t-SNE map (Fig. 1f), we can see that for each group of stars on the map, the edges receive higher weirdness score. We do not want to mark these edges as outliers, and from the t-SNE map we determine that this would be achieved with a threshold of 0.6, for this specific data set. The classification of the outliers is made easier by sorting the objects using their position on the t-SNE map. This way we can classify groups of similar objects instead of one object at a time. Another method we try for outlier inspection is called DEMUD (Wagstaff et al. 2013). Instead of examining the outliers sequentially, one starts from the weirdest object, and then inspects the weird object that is the farthest from the first, followed by the one farthest from the first two, and so forth. The idea is to sample the different populations of outliers quickly, stopping once we start seeing the same types of objects repeating. For the final classification of the outliers, we chose a threshold on the weirdness score (as discussed above) and use the t-SNE map to help with the classification. This is followed by taking a lower threshold on the weirdness score and using DEMUD to look for additional types of outliers. This second step did not result in new types of outliers. 5 APOGEE OUTLIERS In this section, we present the results of manual classification of the highest W250 stars. Here, we use results from both DR14 and DR13, as in DR14 many objects have poorly determined continua. In total, we look at 577 objects. The distribution of the different groups of outliers for DR13 only is shown in Fig. 6. We find the following large groups: Be stars, young stellar objects, carbon enriched stars, double lined spectroscopic binaries (SB2), fast rotators, M dwarfs, M giants and cool K giants (these stars have the highest Wall), and stars with bad spectra. In addition, we find a number of objects that do not fit into any of the above classes. Figure 6. View largeDownload slide Results of the manual classification of 348 highest W250. In the next sections, we discuss each of these groups. The non-GK giants group contains mostly M dwarfs. This figure refers to DR13. Figure 6. View largeDownload slide Results of the manual classification of 348 highest W250. In the next sections, we discuss each of these groups. The non-GK giants group contains mostly M dwarfs. This figure refers to DR13. ‘Bad spectra’ are objects with ASPCAP warn flags, combined with a strange looking spectrum. The flags we encounter for the outliers are commissioning, persist high, and persist jump neg(high). Other ‘bad spectra’ objects are not flagged but have faulty spectra. These appear only in DR14 and we discuss them below. We note that these classes are not mutually exclusive (in Fig. 6 each object is assigned to a single class we believe describes it best). 5.1 B-type emission line stars The objects in this group are Be stars. APOGEE targeted approximately 50 known Be stars in an ancillary program, while the additional Be stars in the APOGEE sample were originally targeted as telluric standard stars. Chojnowski et al. (2015a, 2017) compiled a catalogue of 238 Be stars in the APOGEE data set. They identified these stars by visual inspection. We find 40 Be stars not included in the Chojnowski et al. (2015a) catalogue. These new Be stars first appeared in DR14. For 26 of these stars emission was never reported before. We list these objects in table A2. Some of these stars were detected as outliers, while the rest were found by inspecting the neighbours, in the distance matrix and t-SNE map, of the outliers. As seen in Fig. A1(j), these stars have double peaked H-Br emission lines and weak absorption lines. For some Be stars, metallic emission is also present. ASPCAP fails to derive RVs for these objects, due to their unusual spectra. 5.2 Spectroscopic binaries Example spectra of SB2s are shown in Fig. A1(c) along with their best-fitting synthetic spectra. As seen, ASPCAP does not account for binarity. For some of the SB2s ASPCAP fits broad lines, and for others it fits only one of the two sets of lines. In general, the APOGEE reduction pipeline does not have an automatic binary identification routine (Nidever et al. 2015). Chojnowski et al. (2015b) compiled a catalogue of spectroscopic binaries in APOGEE. 2 The catalogue is constructed by searching for multiple peaks in the spectra cross-correlation function, when comparing to the synthetic template spectra. 15 of the 72 binaries we find as outliers are not listed in the catalogue of Chojnowski et al. (2015b) and are therefore new. 5.3 Fast rotators Broad line stars are also detected as outliers. ASPCAP fits broad lines well for dwarf stars but not for giants, as can be seen in Fig. A1(d). These stars are all flagged with suspect broad lines by the ASPCAP pipeline. 5.4 Carbon rich stars Carbon rich stars (discussed in Section 3.4) are also detected as outliers. In Fig. A1(e), we can see the strong CN compared to OH lines for a few carbon enriched stars. The weirdness score increases with the strength of the CN features. 5.5 Young stellar objects Stars in this group show both H-Br emission lines as well as regular metallic absorption lines. They are mostly young stars included in the INfrared Survey of Young Nebulous Clusters (IN-SYNC, Cottaar et al. 2014). We detect stars with both broad and narrow emission, and with absorption that can be broad or narrow as well as double lined (SB2s). SIMBAD classification for stars in this group include ‘Variable star of Orion type’, ‘T Tau-type Star’, ‘Pre-main sequence Star’, and ‘Young stellar object’. 5.6 M dwarfs M dwarf stars are also detected as outliers. This is due to the small number of M dwarf stars in the APOGEE sample. Example spectra are in Fig. A1(g). 5.7 Other outliers Some of the objects detected as outliers did not fall into any of the above classes. These include a brown dwarf, a Wolf–Rayet star, a few AGB stars including an OH-IR star, and known variable stars, as well as three red supergiants observed in the massive stars ancillary program. Also detected as outliers are special non-stellar targets, such as the centre of M32, a few M31 globular clusters, and three planetary nebulae. Two outliers show double peaked H-Br emission lines, as well as absorption lines typical to the APOGEE data set. Both of these objects show RV modulations, suggesting they are multiple star systems. For the first, 2M04052624+5304494, the RV modulation of the absorption lines (determined from APOGEE visit spectra) could be modelled with a period of P = 11.152 ± 0.072 d, and amplitude of K = 78 ± 15 km s−1. The emission lines in the APOGEE spectra show smaller RV modulation, if any. For the second, 2M06415063−0130177, the absorption lines RV changes by ∼160 km s−1 between two APOGEE visits. The visits are separated by 28 d. For the emission lines we could not get a good estimate on the RVs, as the emission line profiles change significantly between the visits. A CoRoT light-curve is available for this system, showing clear periodic modulation. A period of P = 29.04 d was derived for this light-curve by Affer et al. (2012). We note that this period does not agree with the RV modulation. For both of these systems, additional work is required to determine their nature. We also detect a group of objects with similar, very broad features. Most of these objects have SIMBAD classifications as contact binaries, mainly W Ursae Majoris. A few objects remain unexplained. We divide these objects into two groups. In the first group, we have objects with spectra that seems to have similar features to typical APOGEE red giants (by means of visual inspection). The second group contains stars with spectra that are clearly different from typical APOGEE red giants. We refer to the first group as unexplained red giants, and to the second group as unexplained non-red giants. Some of the unexplained red giants stars have low carbon and high nitrogen ASPCAP abundances. Inspecting their spectra, we do see significantly weaker CO features relative to low weirdness score stars with similar stellar parameters. One of these stars, 2M17534571−2949362, is discussed in Fernández-Trincado et al. (2017) as having low Mg, but high Al and N abundances. There are three unexplained non-red giants, the first is 2M03411288+2453344, which was targeted as a telluric calibrator target. As could be seen in Fig. A1(h), the ASPCAP fit does not catch many of the features in the spectrum, in particular, there is no H-Br absorption. The cross-correlation function shows a single peak, suggesting it is not a binary star. There are three visits to this star, all showing the same features. The objects most similar to this one, according to the distance matrix, do not show similar features. 2M05264478+1049152 has very broad features that we do not identify. Same goes for 2M23375653+8534449, which also has a single emission line centred at $$\lambda = 16055 [{^{\circ}_{\rm A}}]$$ that we cannot identify. We show the spectra of the unexplained non-red giants in Fig. 7. Figure 7. View largeDownload slide Spectra for the three unclassified outliers. Top spectrum is a typical APOGEE red giant, for comparison. The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. Figure 7. View largeDownload slide Spectra for the three unclassified outliers. Top spectrum is a typical APOGEE red giant, for comparison. The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. 5.8 Bad reductions In DR14, roughly half of the high weirdness score objects have badly determined continua. We show a few examples in Fig. A1. These objects can be divided into two groups. For the first group, the issue seems to be a bug in the ASPCAP PCN process. For objects in this group the combined unnormalized spectra looks regular, as well as the DR13 PCN spectra (for objects with available DR13 data). For the second group already at least one of the visit spectra is faulty, and this error propagates down the pipeline. Examples for both of these errors are presented in Fig. A1(i). 6 SUMMARY In this work, we calculate a similarity measure for APOGEE infrared spectra of stars. We show that this similarity matrix traces physical properties such as effective temperature, metallicity, and surface gravity. Such a similarity matrix could be used for object retrieval, i.e. finding objects that are similar to a given example, it can be used to detect outliers, and more generally to assist learning about the structure of a data set. The similarity is obtained without inputing information derived by model fitting, and thus the similarity could be used to query and learn about objects that are not well fitted by the pipeline and as such are hard to find using the fit parameters data base. As noted above, we find that the unsupervised RF is capable of aggregating complex spectral information into a single number, the pair-wise distance between two objects. We find that various stellar parameters are encoded into this distance, and that the resulting RF represents a general model of stellar spectra [Baron & Poznanski (2017) showed that this is true for spectra of galaxies]. As such, one can imagine inverting the process, and using the trained RF to generate ‘real-looking’ objects, which is in turn a generative model. Using this unsupervised RF distance matrix and dimensionality reduction techniques, one can study the structure of the data, and the relations between different classes of objects within a data set. However, it is worth noting that much of the insight gained about the APOGEE sample was made possible using the derived ASPCAP stellar parameters. Without these labels, coloring the t-SNE map would not have been possible. While our proposed unsupervised distance matrix contains various types of information, the extraction of this information still heavily depends on annotations of the distance matrix. Thus, for many applications, it is only the combination of our approach and existing knowledge about the data set that can be useful to gain additional insight. Using our distance matrix to detect outliers, we find objects from the following types of known classes: B-type emission-line stars, carbon rich stars, spectroscopic binaries, broad line stars, young stars, bad spectra, and M dwarfs (which are ordinary but underrepresented in the data set), showing that the algorithm is capable of detecting a wide variety of phenomena. A few dozens of objects that were detected as outliers did not fall into any of the large groups, these include special targets such as galaxies, globular clusters, and planetary nebulae, stars with unusual abundances, contact binaries, stars observed with the massive star ancillary program and more. Three outliers remain without explanation. Some of the carbon rich outliers have a poor ASPCAP fit, though these groups are included in the ASPCAP stellar spectral library. Possibly the objects without a good fit are extreme cases and could be used to improve and test the pipeline. The SB2s detected as outliers have diverse types of spectra and could be used to test SB2 detection specific algorithms. Bad spectra objects and underrepresented objects are not interesting by themselves, but detecting them could be useful in order to clean the sample and find bugs in the pipeline. Finding new Be stars is an example for detection of new objects of known types using the distance matrix or t-SNE map. This is especially useful in larger surveys, where visual inspection is not feasible. The use of t-SNE to visualize the distance matrix was also useful for the purpose of outlier detection. This enabled us to speed up the classification of the outliers by classifying nearby objects together. More importantly, the t-SNE map proved to be useful in learning about the regular objects in the data set, an important step to take before looking at the outliers. Viewing spectra of objects located in different regions of the t-SNE map allowed us to quickly review the different classes of regular objects. For the APOGEE data set, in which there is one large group of similar objects, a nearest neighbours weirdness score, or a ‘local’ weirdness score, was needed in order to detect the interesting outliers. Although this was not required to detect the interesting outlying galaxies in Baron & Poznanski (2017), we believe the local weirdness score is more general and should be used in future work. The number of nearest neighbours to use when calculating the local weirdness score is data set dependent. Coloring the t-SNE map by the different weirdness scores or building t-SNE maps with different perplexities, can help decide on which number of nearest neighbours is appropriate. It is also possible that in order to detect all interesting objects one type of nearest neighbours weirdness score would not be enough, as different types of outliers can come in different (small) cluster sizes. In our case, the outliers population seemed robust to a number of nearest neighbours from a few to a few thousands. We note that for the map shown in Fig. 1, we used perplexity of 2000. This value was chosen in order to make the visualization relatively simple. With smaller perplexity, we obtained maps with more complex small scale structure, such as small clusters. These maps could be useful for investigating the data further but for a clean visualization of the large-scale structure we used a high perplexity map. Future work could involve combining the distance matrix, which is based on spectral data alone, with other types of available data. A natural direction is the physical position of a star. For example, one can look for stars that are normal compared to the entire population of stars, but are weird when compared to their local environment. A table with the 100 nearest neighbours of each object, including their respective distances, is available online. We also include the coordinates for the t-SNE map shown above. Acknowledgements We thank D. Hogg for suggesting the use of t-SNE, and other useful comments, and D. Chojnowski for discussing some of the outliers. We also thank the reviewer for helpful suggestions to improve this manuscript. This research made use of: the NASA Astrophysics Data System Bibliographic Services, scikit-learn (Pedregosa et al. 2011), SciPy (Jones et al. 2001), IPython (Pérez & Granger 2007), matplotlib (Hunter 2007), astropy (Astropy Collaboration et al. 2013), and the SIMBAD data base (Wenger et al. 2000). This work made extensive use of SDSS data. Funding for the SDSS IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. Footnotes 1 Code can be found at https://github.com/dalya/WeirdestGalaxies 2 Their catalogue can be found here http://astronomy.nmsu.edu/drewski/apogee-sb2/apSB2.html REFERENCES Abolfathi B. et al.  , 2017, preprint (arXiv:1707.09322) Affer L., Micela G., Favata F., Flaccomio E., 2012, MNRAS , 424, 11 https://doi.org/10.1111/j.1365-2966.2012.20802.x CrossRef Search ADS   Astropy C ollaboration et al.  , 2013, A&A , 558, A33 https://doi.org/10.1051/0004-6361/201322068 CrossRef Search ADS   Ball N. M., Brunner R. J., 2010, Int. J. Mod. Phys. D , 19, 1049 https://doi.org/10.1142/S0218271810017160 CrossRef Search ADS   Baron D., Poznanski D., 2017, MNRAS , 465, 4530 https://doi.org/10.1093/mnras/stw3021 CrossRef Search ADS   Baron D., Poznanski D., Watson D., Yao Y., Cox N. L. J., Prochaska J. X., 2015, MNRAS , 451, 332 https://doi.org/10.1093/mnras/stv977 CrossRef Search ADS   Baron D., Netzer H., Poznanski D., Prochaska J. X., Förster Schreiber N. M., 2017, MNRAS , 470, 1687 https://doi.org/10.1093/mnras/stx1329 CrossRef Search ADS   Bloom J. S. et al.  , 2012, PASP , 124, 1175 https://doi.org/10.1086/668468 CrossRef Search ADS   Bovy J., 2016, ApJ , 817, 49 https://doi.org/10.3847/0004-637X/817/1/49 CrossRef Search ADS   Bovy J. et al.  , 2014, ApJ , 790, 127 https://doi.org/10.1088/0004-637X/790/2/127 CrossRef Search ADS   Breiman L., 2001, Machine Learning , 45, 5 https://doi.org/10.1023/A:1010933404324 CrossRef Search ADS   Breiman L., Cutler A., 2003, Tech. Rep , Available at: https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf. Breiman L., Friedman J., Olshen R., Stone C., 1984, Classification and Regression Trees . Wadsworth and Brooks, Monterey, CA Chiappini C. et al.  , 2015, A&A , 576, L12 https://doi.org/10.1051/0004-6361/201525865 CrossRef Search ADS   Chojnowski S. D. et al.  , 2015a, AJ , 149, 7 https://doi.org/10.1088/0004-6256/149/1/7 CrossRef Search ADS   Chojnowski S. D. et al.  , 2015b, Am. Astron. Soc. Meeting Abstr. , p. 340 Chojnowski S. D. et al.  , 2017, AJ , 153, 174 https://doi.org/10.3847/1538-3881/aa64ce CrossRef Search ADS   Cottaar M. et al.  , 2014, ApJ , 794, 125 https://doi.org/10.1088/0004-637X/794/2/125 CrossRef Search ADS   Eisenstein D. J. et al.  , 2011, AJ , 142, 72 https://doi.org/10.1088/0004-6256/142/3/72 CrossRef Search ADS   Fernández-Trincado J. G. et al.  , 2017, ApJ , 846, L2 https://doi.org/10.3847/2041-8213/aa8032 CrossRef Search ADS   Frinchaboy P. M. et al.  , 2013, ApJ , 777, L1 https://doi.org/10.1088/2041-8205/777/1/L1 CrossRef Search ADS   García Pérez A. E. et al.  , 2016, AJ , 151, 144 https://doi.org/10.3847/0004-6256/151/6/144 CrossRef Search ADS   Garcia-Dias R., Allende Prieto C., Sánchez Almeida J., Ordovás-Pascual I., 2018, preprint (arXiv:1801.07912) Hayden M. R. et al.  , 2015, ApJ , 808, 132 https://doi.org/10.1088/0004-637X/808/2/132 CrossRef Search ADS   Hunter J. D., 2007, Comput. Sci. Eng. , 9, 90 https://doi.org/10.1109/MCSE.2007.55 CrossRef Search ADS   Jofré P., Mädler T., Gilmore G., Casey A. R., Soubiran C., Worley C., 2015, MNRAS , 453, 1428 https://doi.org/10.1093/mnras/stv1724 CrossRef Search ADS   Jofré P. et al.  , 2017, MNRAS , 472, 2517 https://doi.org/10.1093/mnras/stx1877 CrossRef Search ADS   Jones E. et al.  , 2001, SciPy: Open source scientific tools for Python , Available at: http://www.scipy.org/ Knorr E. M., Ng R. T., 1999, in Proceedings of the 25th International Conference on Very Large Data Bases. VLDB’99 . Morgan Kaufmann Publishers Inc. , San Francisco, CA, USA, pp 211– 222. Available at: http://dl.acm.org/citation.cfm?id=645925.671529 Knorr E. M., Ng R. T., Tucakov V., 2000, The VLDB Journal , 8, 237 https://doi.org/10.1007/s007780050006 CrossRef Search ADS   Majewski S. R., APOGEE Team APOGEE-2 Team, 2016, Astron. Nachr. , 337, 863 https://doi.org/10.1002/asna.201612387 CrossRef Search ADS   Masci F. J., Hoffman D. I., Grillmair C. J., Cutri R. M., 2014, AJ , 148, 21 https://doi.org/10.1088/0004-6256/148/1/21 CrossRef Search ADS   Meusinger H., Schalldach P., Scholz R.-D., in der Au A., Newholm M., de Hoon A., Kaminsky B., 2012, A&A , 541, A77 https://doi.org/10.1051/0004-6361/201118143 CrossRef Search ADS   Miller A. A., Kulkarni M. K., Cao Y., Laher R. R., Masci F. J., Surace J. A., 2017, AJ , 153, 73 https://doi.org/10.3847/1538-3881/153/2/73 CrossRef Search ADS   Ness M., Hogg D. W., Rix H.-W., Ho A. Y. Q., Zasowski G., 2015, ApJ , 808, 16 https://doi.org/10.1088/0004-637X/808/1/16 CrossRef Search ADS   Nidever D. L. et al.  , 2014, ApJ , 796, 38 https://doi.org/10.1088/0004-637X/796/1/38 CrossRef Search ADS   Nidever D. L. et al.  , 2015, AJ , 150, 173 https://doi.org/10.1088/0004-6256/150/6/173 CrossRef Search ADS   Pedregosa F. et al.  , 2011, J. Mach. Learn. Res. , 12, 2825 Pérez F., Granger B. E., 2007, Comput. Sci. Eng. , 9, 21 CrossRef Search ADS   Pimentel M. A., Clifton D. A., Clifton L., Tarassenko L., 2014, Signal Process. , 99, 215 https://doi.org/10.1016/j.sigpro.2013.12.026 CrossRef Search ADS   Schawinski K., Zhang C., Zhang H., Fowler L., Santhanam G. K., 2017, MNRAS , 467, L110 Shi T., Horvath S., 2006, J. Comput. Graph. Stat. , 15, 118 https://doi.org/10.1198/106186006X94072 CrossRef Search ADS   van der Maaten L. J. P., Hinton G. E., 2008, J. Mach. Learn. Res. , 9, 2579 Wagstaff K. L., Lanza N. L., Thompson D. R., Dietterich T. G., Gilmore M. S., 2013, in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI’13 . AAAI Press, pp 905– 911. Available at: http://dl.acm.org/citation.cfm?id=2891460.2891586 Wattenberg M., Viégas F., Johnson I., 2016, Distill  Wenger M. et al.  , 2000, A&AS , 143, 9 CrossRef Search ADS   Yang L., 2006, Distance Metric Learning: A Comprehensive Survey . Available at: http://www.cs.cmu.edu/~liuy/frame_survey_v2.pdf Zasowski G. et al.  , 2013, AJ , 146, 81 https://doi.org/10.1088/0004-6256/146/4/81 CrossRef Search ADS   Zasowski G. et al.  , 2017, AJ , 154, 198 https://doi.org/10.3847/1538-3881/aa8df9 CrossRef Search ADS   APPENDIX A: SPECTRA AND TABLES In Fig. A1, we show example spectra of objects from the different outlying groups, as well as spectroscopic twins. Figure A1. View largeDownload slide Example spectra for different groups of objects. The spectra plots were made using the APOGEE toolkit by Bovy (2016). The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. In every panel, we choose the most informative wavelength range. (a) Regular objects, i.e. objects with low weirdness scores. Clearly, all have similar spectra. PR(W250) indicates the percentile of objects with lower weirdness score. (b) Three example pairs of spectroscopic twins. The twin spectra are over-plotted, one in green and the other in blue. Note that in the top example one of the stars lacks an ASPCAP fit, preventing identification as a twin via these parameters. (c) Spectroscopic binaries. (d) Fast rotators. Top spectrum is a typical APOGEE red giant, for comparison. (e) Carbon enriched stars. Top spectrum is a typical APOGEE red giant, for comparison. (f) Stars with both absorption and hydrogen emission. Top spectrum is a typical APOGEE red giant, for comparison. We see both narrow and broad emission stars, and also both narrow and broad absorption. The second spectra from the top is also an SB2. The bottom spectrum has bad RV determination. (g) M dwarfs. Top spectrum is a typical APOGEE red giant, for comparison. M dwarfs are detected as outliers due to their underrepresentation in the APOGEE data set. (h) Stars from the ‘others’ pile. Top spectrum is a typical APOGEE red giant, for comparison. Starting from the second from top, the four outlying spectra are brown dwarf, massive star target, unexplained red giant, and a Wolf–Rayet star. (i)DR14 Faulty spectra. For the top two objects, the problems are due to an issue in the PCN process, for the bottom three one of the visit spectra is bad. (j) B-type emission line stars showing double peaked hydrogen emission. The emission lines are not on the dotted lines due to wrong RV determination by the APOGEE pipeline. Figure A1. View largeDownload slide Example spectra for different groups of objects. The spectra plots were made using the APOGEE toolkit by Bovy (2016). The red line is the ASPCAP fit and the blue line is the PCN spectrum, except where indicated. PR(W250) indicates the percentile of objects with lower weirdness score. Relative fluxes are offset by a constant for display purposes. In every panel, we choose the most informative wavelength range. (a) Regular objects, i.e. objects with low weirdness scores. Clearly, all have similar spectra. PR(W250) indicates the percentile of objects with lower weirdness score. (b) Three example pairs of spectroscopic twins. The twin spectra are over-plotted, one in green and the other in blue. Note that in the top example one of the stars lacks an ASPCAP fit, preventing identification as a twin via these parameters. (c) Spectroscopic binaries. (d) Fast rotators. Top spectrum is a typical APOGEE red giant, for comparison. (e) Carbon enriched stars. Top spectrum is a typical APOGEE red giant, for comparison. (f) Stars with both absorption and hydrogen emission. Top spectrum is a typical APOGEE red giant, for comparison. We see both narrow and broad emission stars, and also both narrow and broad absorption. The second spectra from the top is also an SB2. The bottom spectrum has bad RV determination. (g) M dwarfs. Top spectrum is a typical APOGEE red giant, for comparison. M dwarfs are detected as outliers due to their underrepresentation in the APOGEE data set. (h) Stars from the ‘others’ pile. Top spectrum is a typical APOGEE red giant, for comparison. Starting from the second from top, the four outlying spectra are brown dwarf, massive star target, unexplained red giant, and a Wolf–Rayet star. (i)DR14 Faulty spectra. For the top two objects, the problems are due to an issue in the PCN process, for the bottom three one of the visit spectra is bad. (j) B-type emission line stars showing double peaked hydrogen emission. The emission lines are not on the dotted lines due to wrong RV determination by the APOGEE pipeline. Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued Figure A1 View largeDownload slide – continued In Table A1, we present all the objects detected as outliers, and did not fall into any of the large groups. Tables with the objects in the rest of the groups are available online. Table A1. Outliers that and did not fall into any of the large groups. APOGEE ID  RA (°)  Dec. (°)  Classification  2M15010818+2250020  225.284  22.8339  Brown dwarf  2M14323054+5049406  218.127  50.828  Contact binary  2M03114116−0043477  47.9215  −0.72993  Contact binary  2M14304297+0905087  217.679  9.08575  Contact binary  2M13465180+2257140  206.716  22.9539  Contact binary  2M14120965+0508201  213.04  5.13893  Contact binary  2M16145863+3016356  243.744  30.2766  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M16241043+4555265  246.043  45.924  Contact binary  2M16524137+4723275  253.172  47.391  Contact binary  2M06415063−0130177  100.461  −1.50494  Double-peaked emission  2M04052624+5304494  61.3594  53.0804  Double-peaked emission  2M13145725+1713303  198.739  17.2251  Galaxy  AP00425080+4117074  10.7117  41.2854  Globular cluster  AP00442956+4121359  11.1232  41.36  Globular cluster  AP00430957+4121321  10.7899  41.3589  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00431764+4127450  10.8235  41.4625  Globular cluster  2M18445087−0325251  281.212  −3.42364  Massive star  2M18452141−0330149  281.339  −3.50416  Massive star  2M18440079-0353160  281.003  −3.88778  Massive star  2M03411288+2453344  55.3037  24.8929  Unexplained non-red giant  2M05264478+1049152  81.6866  10.8209  Unexplained non-red giant  2M23375653+8534449  354.486  85.5792  Unexplained non-red giant  2M04255084+6007127  66.4619  60.1202  Planetary nebula  2M21021878+3641412  315.578  36.6948  Planetary nebula–Egg nebula  2M18211606−1301256  275.317  −13.0238  Planetary nebula–Red Square nebula  2M17534571−2949362  268.44  −29.8267  Unexplained red giant  2M06361326+0919120  99.0553  9.32001  Unexplained red giant  2M00220008+6915238  5.50037  69.2566  Unexplained red giant  2M21184119+4836167  319.672  48.6047  Unexplained red giant  2M20564714+5013372  314.196  50.227  Unexplained red giant  2M05501847−0010369  87.577  −0.176939  Unexplained red giant  2M23001010+6055385  345.042  60.9274  Wolf–Rayet star  2M05473667+0020060  86.9028  0.33501  Young stellar object  APOGEE ID  RA (°)  Dec. (°)  Classification  2M15010818+2250020  225.284  22.8339  Brown dwarf  2M14323054+5049406  218.127  50.828  Contact binary  2M03114116−0043477  47.9215  −0.72993  Contact binary  2M14304297+0905087  217.679  9.08575  Contact binary  2M13465180+2257140  206.716  22.9539  Contact binary  2M14120965+0508201  213.04  5.13893  Contact binary  2M16145863+3016356  243.744  30.2766  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M03242324−0042148  51.0969  −0.704119  Contact binary  2M16241043+4555265  246.043  45.924  Contact binary  2M16524137+4723275  253.172  47.391  Contact binary  2M06415063−0130177  100.461  −1.50494  Double-peaked emission  2M04052624+5304494  61.3594  53.0804  Double-peaked emission  2M13145725+1713303  198.739  17.2251  Galaxy  AP00425080+4117074  10.7117  41.2854  Globular cluster  AP00442956+4121359  11.1232  41.36  Globular cluster  AP00430957+4121321  10.7899  41.3589  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00424183+4051550  10.6743  40.8653  Globular cluster  AP00431764+4127450  10.8235  41.4625  Globular cluster  2M18445087−0325251  281.212  −3.42364  Massive star  2M18452141−0330149  281.339  −3.50416  Massive star  2M18440079-0353160  281.003  −3.88778  Massive star  2M03411288+2453344  55.3037  24.8929  Unexplained non-red giant  2M05264478+1049152  81.6866  10.8209  Unexplained non-red giant  2M23375653+8534449  354.486  85.5792  Unexplained non-red giant  2M04255084+6007127  66.4619  60.1202  Planetary nebula  2M21021878+3641412  315.578  36.6948  Planetary nebula–Egg nebula  2M18211606−1301256  275.317  −13.0238  Planetary nebula–Red Square nebula  2M17534571−2949362  268.44  −29.8267  Unexplained red giant  2M06361326+0919120  99.0553  9.32001  Unexplained red giant  2M00220008+6915238  5.50037  69.2566  Unexplained red giant  2M21184119+4836167  319.672  48.6047  Unexplained red giant  2M20564714+5013372  314.196  50.227  Unexplained red giant  2M05501847−0010369  87.577  −0.176939  Unexplained red giant  2M23001010+6055385  345.042  60.9274  Wolf–Rayet star  2M05473667+0020060  86.9028  0.33501  Young stellar object  View Large In Table A2, we list Be stars which are new in DR14 and thus not included in the Chojnowski et al. (2015a) catalogue. Table A2. Be stars. APOGEE ID  RA (°)  Dec. (°)  2M20383016+2119439  309.626  21.3289  2M22425730+4443183  340.739  44.7218  2M06490825+0005220  102.284  0.089448  2M04480651+3359160  72.0271  33.9878  2M05284845+0209529  82.2019  2.16471  2M21582976+5429057  329.624  54.4849  2M22082542+5413262  332.106  54.2239  2M19322817−0454283  293.117  −4.90786  2M03145531+4841448  48.7305  48.6958  2M21523408+4713436  328.142  47.2288  2M04454937+4323302  71.4557  43.3917  2M18574904+1758251  284.454  17.9736  2M05312677+1101226  82.8616  11.0229  2M05384719−0235405  84.6967  −2.59459  2M22075623+5431064  331.984  54.5185  2M21380289+5037030  324.512  50.6175  2M06521036-0017440  103.043  −0.29556  2M04125427+6647203  63.2262  66.789  2M22142219+4206020  333.592  42.1006  2M04493134+3313091  72.3806  33.2192  2M02374876+5248458  39.4532  52.8127  2M05122466+4816538  78.1028  48.2816  2M23570808+6118272  359.284  61.3076  2M05441926+5241437  86.0803  52.6955  2M18042714−0958113  271.113  −9.96982  2M06514059+0019363  102.919  0.326773  2M02273460+4813548  36.8942  48.2319  2M23293672+4822513  352.403  48.3809  2M18040936−0827329  271.039  −8.45915  2M21151579+3235270  318.816  32.5909  2M06552851+2430188  103.869  24.5052  2M19575932+2714001  299.497  27.2334  2M04563331+6345566  74.1388  63.7657  2M19562230+2626258  299.093  26.4405  2M10214707+1532036  155.446  15.5344  2M05271779+1308569  81.8241  13.1492  2M02571539+4601118  44.3142  46.02  2M21504079+5518451  327.67  55.3125  2M04503901+3243187  72.6626  32.7219  2M22165865+6738450  334.244  67.6458  APOGEE ID  RA (°)  Dec. (°)  2M20383016+2119439  309.626  21.3289  2M22425730+4443183  340.739  44.7218  2M06490825+0005220  102.284  0.089448  2M04480651+3359160  72.0271  33.9878  2M05284845+0209529  82.2019  2.16471  2M21582976+5429057  329.624  54.4849  2M22082542+5413262  332.106  54.2239  2M19322817−0454283  293.117  −4.90786  2M03145531+4841448  48.7305  48.6958  2M21523408+4713436  328.142  47.2288  2M04454937+4323302  71.4557  43.3917  2M18574904+1758251  284.454  17.9736  2M05312677+1101226  82.8616  11.0229  2M05384719−0235405  84.6967  −2.59459  2M22075623+5431064  331.984  54.5185  2M21380289+5037030  324.512  50.6175  2M06521036-0017440  103.043  −0.29556  2M04125427+6647203  63.2262  66.789  2M22142219+4206020  333.592  42.1006  2M04493134+3313091  72.3806  33.2192  2M02374876+5248458  39.4532  52.8127  2M05122466+4816538  78.1028  48.2816  2M23570808+6118272  359.284  61.3076  2M05441926+5241437  86.0803  52.6955  2M18042714−0958113  271.113  −9.96982  2M06514059+0019363  102.919  0.326773  2M02273460+4813548  36.8942  48.2319  2M23293672+4822513  352.403  48.3809  2M18040936−0827329  271.039  −8.45915  2M21151579+3235270  318.816  32.5909  2M06552851+2430188  103.869  24.5052  2M19575932+2714001  299.497  27.2334  2M04563331+6345566  74.1388  63.7657  2M19562230+2626258  299.093  26.4405  2M10214707+1532036  155.446  15.5344  2M05271779+1308569  81.8241  13.1492  2M02571539+4601118  44.3142  46.02  2M21504079+5518451  327.67  55.3125  2M04503901+3243187  72.6626  32.7219  2M22165865+6738450  334.244  67.6458  View Large In Table A3, we list carbon rich stars that were detected as outliers. Table A3. Carbon rich stars. APOGEE ID  RA (°)  Dec. (°)  2M21095891+1111013  317.495  11.1837  2M12553245+4328014  193.885  43.4671  2M08031240+5311340  120.802  53.1928  2M17552511−2517291  268.855  −25.2914  2M18442763−0614402  281.115  −6.24452  2M06211564−0124429  95.3152  −1.41194  2M12410240−0853066  190.26  −8.88517  2M13150364+1806426  198.765  18.1119  2M07384226+2131021  114.676  21.5173  2M05264861+2551545  81.7026  25.8652  2M16334467−1343201  248.436  −13.7223  2M13381781−1458456  204.574  −14.9793  2M13122536+1313575  198.106  13.2327  2M15000319+2955500  225.013  29.9306  2M21330683+1209406  323.278  12.1613  2M18455347−0328585  281.473  −3.48293  2M18495015−0235162  282.459  −2.58786  2M19425134+2235573  295.714  22.5993  2M19474632+2349074  296.943  23.8187  2M00242588+6221034  6.10785  62.3509  2M04501927+3947587  72.5803  39.7996  2M05012902+4023388  75.3709  40.3941  2M21053099+2952201  316.379  29.8723  2M01403590+6254392  25.1496  62.9109  2M04405098+4705190  70.2124  47.0886  2M18191371−1218145  274.807  −12.304  2M18030503−2157460  270.771  −21.9628  2M18015024−2638220  270.459  −26.6395  2M17520031−2308488  268.001  −23.1469  2M18052874−2505351  271.37  −25.0931  2M18063056−2435442  271.627  −24.5956  2M18111704−2352577  272.821  −23.8827  2M18115753−1503100  272.99  −15.0528  2M18185547−1119080  274.731  −11.3189  2M18524968−2834454  283.207  −28.5793  2M17301939−2913292  262.581  −29.2248  2M19295061+0010102  292.461  0.16951  2M19411240+4936344  295.302  49.6096  2M19003459+4408290  285.144  44.1414  2M19023427+4246148  285.643  42.7708  2M19095794+4325272  287.491  43.4242  2M06343313+0643006  98.6381  6.71686  2M01471583+5753060  26.816  57.885  2M21473632+5932259  326.901  59.5405  2M21544864+5916346  328.703  59.2763  2M11475977−0019182  176.999  −0.32173  2M18284700−1010553  277.196  −10.182  2M17043371−2212322  256.14  −22.209  2M19233187+4405575  290.883  44.0993  2M12553245+4328014  193.885  43.4671  2M14561660+1702441  224.069  17.0456  2M15015733+2713595  225.489  27.2332  2M15100330+3054073  227.514  30.902  2M23290070+5711558  352.253  57.1989  2M02403149+5600473  40.1312  56.0132  2M07094794+0006382  107.45  0.11062  2M07232483−0823577  110.853  −8.39936  2M03463234+3221127  56.6348  32.3536  2M19531095+4635518  298.296  46.5977  2M19200927+1317078  290.039  13.2855  2M21315424+5219122  322.976  52.3201  2M00334926+6837330  8.45529  68.6258  2M04174731+4211335  64.4472  42.1926  2M04195310+4109094  64.9713  41.1526  2M06372981+0515011  99.3742  5.25033  2M00373109+5743345  9.37956  57.7263  2M03244820+6300289  51.2009  63.008  2M03271166+6240211  51.7986  62.6725  2M03330010+6330443  53.2504  63.5123  2M03395727+6227241  54.9886  62.4567  2M06393827+2403560  99.9095  24.0656  2M05152962+2400147  78.8734  24.0041  2M03103113+4831002  47.6297  48.5167  2M03221451+4756591  50.5605  47.9498  2M05522651+4329557  88.1105  43.4988  2M20553607+5613011  313.9  56.217  2M21031081+5414127  315.795  54.2369  2M21084459+5442122  317.186  54.7034  2M21590597+4539010  329.775  45.6503  2M21554492+5414593  328.937  54.2498  2M21573025+5440529  329.376  54.6814  2M21594113+5351121  329.921  53.8534  2M22085910+5434192  332.246  54.572  2M18300408+0416050  277.517  4.26807  2M04113023+2255071  62.876  22.9187  2M06531594−0439506  103.316  −4.66407  APOGEE ID  RA (°)  Dec. (°)  2M21095891+1111013  317.495  11.1837  2M12553245+4328014  193.885  43.4671  2M08031240+5311340  120.802  53.1928  2M17552511−2517291  268.855  −25.2914  2M18442763−0614402  281.115  −6.24452  2M06211564−0124429  95.3152  −1.41194  2M12410240−0853066  190.26  −8.88517  2M13150364+1806426  198.765  18.1119  2M07384226+2131021  114.676  21.5173  2M05264861+2551545  81.7026  25.8652  2M16334467−1343201  248.436  −13.7223  2M13381781−1458456  204.574  −14.9793  2M13122536+1313575  198.106  13.2327  2M15000319+2955500  225.013  29.9306  2M21330683+1209406  323.278  12.1613  2M18455347−0328585  281.473  −3.48293  2M18495015−0235162  282.459  −2.58786  2M19425134+2235573  295.714  22.5993  2M19474632+2349074  296.943  23.8187  2M00242588+6221034  6.10785  62.3509  2M04501927+3947587  72.5803  39.7996  2M05012902+4023388  75.3709  40.3941  2M21053099+2952201  316.379  29.8723  2M01403590+6254392  25.1496  62.9109  2M04405098+4705190  70.2124  47.0886  2M18191371−1218145  274.807  −12.304  2M18030503−2157460  270.771  −21.9628  2M18015024−2638220  270.459  −26.6395  2M17520031−2308488  268.001  −23.1469  2M18052874−2505351  271.37  −25.0931  2M18063056−2435442  271.627  −24.5956  2M18111704−2352577  272.821  −23.8827  2M18115753−1503100  272.99  −15.0528  2M18185547−1119080  274.731  −11.3189  2M18524968−2834454  283.207  −28.5793  2M17301939−2913292  262.581  −29.2248  2M19295061+0010102  292.461  0.16951  2M19411240+4936344  295.302  49.6096  2M19003459+4408290  285.144  44.1414  2M19023427+4246148  285.643  42.7708  2M19095794+4325272  287.491  43.4242  2M06343313+0643006  98.6381  6.71686  2M01471583+5753060  26.816  57.885  2M21473632+5932259  326.901  59.5405  2M21544864+5916346  328.703  59.2763  2M11475977−0019182  176.999  −0.32173  2M18284700−1010553  277.196  −10.182  2M17043371−2212322  256.14  −22.209  2M19233187+4405575  290.883  44.0993  2M12553245+4328014  193.885  43.4671  2M14561660+1702441  224.069  17.0456  2M15015733+2713595  225.489  27.2332  2M15100330+3054073  227.514  30.902  2M23290070+5711558  352.253  57.1989  2M02403149+5600473  40.1312  56.0132  2M07094794+0006382  107.45  0.11062  2M07232483−0823577  110.853  −8.39936  2M03463234+3221127  56.6348  32.3536  2M19531095+4635518  298.296  46.5977  2M19200927+1317078  290.039  13.2855  2M21315424+5219122  322.976  52.3201  2M00334926+6837330  8.45529  68.6258  2M04174731+4211335  64.4472  42.1926  2M04195310+4109094  64.9713  41.1526  2M06372981+0515011  99.3742  5.25033  2M00373109+5743345  9.37956  57.7263  2M03244820+6300289  51.2009  63.008  2M03271166+6240211  51.7986  62.6725  2M03330010+6330443  53.2504  63.5123  2M03395727+6227241  54.9886  62.4567  2M06393827+2403560  99.9095  24.0656  2M05152962+2400147  78.8734  24.0041  2M03103113+4831002  47.6297  48.5167  2M03221451+4756591  50.5605  47.9498  2M05522651+4329557  88.1105  43.4988  2M20553607+5613011  313.9  56.217  2M21031081+5414127  315.795  54.2369  2M21084459+5442122  317.186  54.7034  2M21590597+4539010  329.775  45.6503  2M21554492+5414593  328.937  54.2498  2M21573025+5440529  329.376  54.6814  2M21594113+5351121  329.921  53.8534  2M22085910+5434192  332.246  54.572  2M18300408+0416050  277.517  4.26807  2M04113023+2255071  62.876  22.9187  2M06531594−0439506  103.316  −4.66407  View Large In Table A4, we list the spectroscopic binaries that were detected as outliers. Table A4. Spectroscopic binaries. APOGEE ID  RA (°)  Dec. (°)  2M14251536+3915337  216.314  39.2594  2M08115373+3212036  122.974  32.201  2M12274221+0002386  186.926  0.044058  2M05284223+4359528  82.176  43.998  2M05240837+2711064  81.0349  27.1851  2M03563567+7857072  59.1487  78.952  2M09314691+5618248  142.945  56.3069  2M13413548−1723167  205.398  −17.388  2M01193634+8435481  19.9014  84.5967  2M14542303+3122323  223.596  31.3756  2M09315645+3714213  142.985  37.2393  2M10280514+1735219  157.021  17.5894  2M11081296-1205110  167.054  −12.0864  2M21302403+1132483  322.6  11.5468  2M15002128+3645004  225.089  36.7501  2M18460678-0337057  281.528  −3.61827  2M19430973+2357587  295.791  23.9663  2M19225746+3824509  290.739  38.4141  2M21523747+3853140  328.156  38.8872  2M18054943−3059442  271.456  −30.9956  2M18075069−3116452  271.961  −31.2792  2M18081808−2553287  272.075  −25.8913  2M17561341−2921380  269.056  −29.3606  2M17360668−2710099  264.028  −27.1694  2M18103554−1811011  272.648  −18.1836  2M18192203−1411326  274.842  −14.1924  2M18192899−1452043  274.871  −14.8679  2M18280206−1217422  277.009  −12.2951  2M17345651−2048568  263.735  −20.8158  2M18001201−2631398  270.05  −26.5277  2M18041435−2455385  271.06  −24.9274  2M18165573−1852394  274.232  −18.8776  2M18040248−1805575  271.01  −18.0993  2M17464152−2713191  266.673  −27.222  2M17531813−2816161  268.326  −28.2711  2M18042203−2917298  271.092  −29.2916  2M17282574−2906578  262.107  −29.1161  2M17285197−2815064  262.217  −28.2518  2M18104783−2824046  272.699  −28.4013  2M19383737+4957227  294.656  49.9563  2M19454606+5113275  296.442  51.2243  2M19301580+4932086  292.566  49.5357  2M18544916+4512355  283.705  45.2099  2M19123630+4603326  288.151  46.0591  2M01593686+6533283  29.9036  65.5579  2M14370236+0928340  219.26  9.47612  2M15021575+2319460  225.566  23.3295  2M11254661+5217235  171.444  52.2899  2M11012916+1215329  165.372  12.2592  2M13405651+0031563  205.235  0.532321  2M18411589−1016542  280.316  −10.2817  2M19564877+4458058  299.203  44.9683  2M20034832+4536148  300.951  45.6041  2M19190180+4153127  289.758  41.8869  2M19561994+4120265  299.083  41.3407  2M13483079+1750445  207.128  17.8457  2M12115853+1425463  182.994  14.4295  2M12462044+1251325  191.585  12.8591  2M12505092+1324147  192.712  13.4041  2M14123798+5426481  213.158  54.4467  2M11542519+5554150  178.605  55.9042  2M11044917+4840467  166.205  48.6797  2M14232001+0541233  215.833  5.68982  2M16582628+0939165  254.61  9.65459  2M09242547−0650183  141.106  −6.83842  2M19400944+3832454  295.039  38.546  2M00065508+0154022  1.72953  1.90061  2M05502340+0420349  87.5975  4.34304  2M07054011+3812529  106.417  38.2147  2M07250686+2435451  111.279  24.5959  2M05345563−0601036  83.7318  −6.01768  2M05350392−0529033  83.7663  −5.48426  2M05360185−0517365  84.0077  −5.29349  2M05350138−0615175  83.7558  −6.25487  2M05351236−0543184  83.8015  −5.7218  2M05351561−0524030  83.8151  −5.40085  2M05351798−0604430  83.8249  −6.07862  2M05371161−0723239  84.2984  −7.38999  2M19383668+4723194  294.653  47.3887  2M18534305+0026394  283.429  0.444304  2M04135110+4938317  63.463  49.6422  2M03361242+4651208  54.0518  46.8558  2M17393731−2324309  264.905  −23.4086  2M17340500−2808243  263.521  −28.1401  2M18234612−1501159  275.942  −15.0211  2M17190649−2745172  259.777  −27.7548  2M17380171−2858281  264.507  −28.9745  2M17535762−2841520  268.49  −28.6978  2M17144370−2449231  258.682  −24.8231  2M17364991−2728343  264.208  −27.4762  2M19252567+4229371  291.357  42.4936  APOGEE ID  RA (°)  Dec. (°)  2M14251536+3915337  216.314  39.2594  2M08115373+3212036  122.974  32.201  2M12274221+0002386  186.926  0.044058  2M05284223+4359528  82.176  43.998  2M05240837+2711064  81.0349  27.1851  2M03563567+7857072  59.1487  78.952  2M09314691+5618248  142.945  56.3069  2M13413548−1723167  205.398  −17.388  2M01193634+8435481  19.9014  84.5967  2M14542303+3122323  223.596  31.3756  2M09315645+3714213  142.985  37.2393  2M10280514+1735219  157.021  17.5894  2M11081296-1205110  167.054  −12.0864  2M21302403+1132483  322.6  11.5468  2M15002128+3645004  225.089  36.7501  2M18460678-0337057  281.528  −3.61827  2M19430973+2357587  295.791  23.9663  2M19225746+3824509  290.739  38.4141  2M21523747+3853140  328.156  38.8872  2M18054943−3059442  271.456  −30.9956  2M18075069−3116452  271.961  −31.2792  2M18081808−2553287  272.075  −25.8913  2M17561341−2921380  269.056  −29.3606  2M17360668−2710099  264.028  −27.1694  2M18103554−1811011  272.648  −18.1836  2M18192203−1411326  274.842  −14.1924  2M18192899−1452043  274.871  −14.8679  2M18280206−1217422  277.009  −12.2951  2M17345651−2048568  263.735  −20.8158  2M18001201−2631398  270.05  −26.5277  2M18041435−2455385  271.06  −24.9274  2M18165573−1852394  274.232  −18.8776  2M18040248−1805575  271.01  −18.0993  2M17464152−2713191  266.673  −27.222  2M17531813−2816161  268.326  −28.2711  2M18042203−2917298  271.092  −29.2916  2M17282574−2906578  262.107  −29.1161  2M17285197−2815064  262.217  −28.2518  2M18104783−2824046  272.699  −28.4013  2M19383737+4957227  294.656  49.9563  2M19454606+5113275  296.442  51.2243  2M19301580+4932086  292.566  49.5357  2M18544916+4512355  283.705  45.2099  2M19123630+4603326  288.151  46.0591  2M01593686+6533283  29.9036  65.5579  2M14370236+0928340  219.26  9.47612  2M15021575+2319460  225.566  23.3295  2M11254661+5217235  171.444  52.2899  2M11012916+1215329  165.372  12.2592  2M13405651+0031563  205.235  0.532321  2M18411589−1016542  280.316  −10.2817  2M19564877+4458058  299.203  44.9683  2M20034832+4536148  300.951  45.6041  2M19190180+4153127  289.758  41.8869  2M19561994+4120265  299.083  41.3407  2M13483079+1750445  207.128  17.8457  2M12115853+1425463  182.994  14.4295  2M12462044+1251325  191.585  12.8591  2M12505092+1324147  192.712  13.4041  2M14123798+5426481  213.158  54.4467  2M11542519+5554150  178.605  55.9042  2M11044917+4840467  166.205  48.6797  2M14232001+0541233  215.833  5.68982  2M16582628+0939165  254.61  9.65459  2M09242547−0650183  141.106  −6.83842  2M19400944+3832454  295.039  38.546  2M00065508+0154022  1.72953  1.90061  2M05502340+0420349  87.5975  4.34304  2M07054011+3812529  106.417  38.2147  2M07250686+2435451  111.279  24.5959  2M05345563−0601036  83.7318  −6.01768  2M05350392−0529033  83.7663  −5.48426  2M05360185−0517365  84.0077  −5.29349  2M05350138−0615175  83.7558  −6.25487  2M05351236−0543184  83.8015  −5.7218  2M05351561−0524030  83.8151  −5.40085  2M05351798−0604430  83.8249  −6.07862  2M05371161−0723239  84.2984  −7.38999  2M19383668+4723194  294.653  47.3887  2M18534305+0026394  283.429  0.444304  2M04135110+4938317  63.463  49.6422  2M03361242+4651208  54.0518  46.8558  2M17393731−2324309  264.905  −23.4086  2M17340500−2808243  263.521  −28.1401  2M18234612−1501159  275.942  −15.0211  2M17190649−2745172  259.777  −27.7548  2M17380171−2858281  264.507  −28.9745  2M17535762−2841520  268.49  −28.6978  2M17144370−2449231  258.682  −24.8231  2M17364991−2728343  264.208  −27.4762  2M19252567+4229371  291.357  42.4936  View Large In Table A5, we list the spectroscopic binaries that were detected as outliers. Table A5. Fast rotators. APOGEE ID  RA (°)  Dec. (°)  2M21031344+0942207  315.806  9.70577  2M07365631+4517467  114.235  45.2963  2M07560603+2626563  119.025  26.449  2M17550303−2557141  268.763  −25.9539  2M18423451−0422454  280.644  −4.3793  2M11431652+0047511  175.819  0.797531  2M04131296+5546540  63.304  55.7817  2M03283689+7947391  52.1537  79.7942  2M16132421+5140269  243.351  51.6742  2M13553588+4436441  208.9  44.6123  2M18451898−0150567  281.329  −1.84909  2M19421896+2426209  295.579  24.4392  2M21131747+4843554  318.323  48.7321  2M20111813+2058271  302.826  20.9742  2M03464878+2304074  56.7033  23.0687  2M19142629+1202560  288.61  12.0489  2M20204714+3702309  305.196  37.0419  2M19014937+0520105  285.456  5.33626  2M03220356+5654161  50.5148  56.9045  2M17434496−2941008  265.937  −29.6836  2M18142425−1911037  273.601  −19.1844  2M18202527−1537239  275.105  −15.6233  2M18313707−1222341  277.904  −12.3761  2M19154842+4636261  288.952  46.6073  2M19544569+4041406  298.69  40.6946  2M20000263+4529265  300.011  45.4907  2M18543899+0012432  283.662  0.212021  2M19522028+2723553  298.085  27.3987  APOGEE ID  RA (°)  Dec. (°)  2M21031344+0942207  315.806  9.70577  2M07365631+4517467  114.235  45.2963  2M07560603+2626563  119.025  26.449  2M17550303−2557141  268.763  −25.9539  2M18423451−0422454  280.644  −4.3793  2M11431652+0047511  175.819  0.797531  2M04131296+5546540  63.304  55.7817  2M03283689+7947391  52.1537  79.7942  2M16132421+5140269  243.351  51.6742  2M13553588+4436441  208.9  44.6123  2M18451898−0150567  281.329  −1.84909  2M19421896+2426209  295.579  24.4392  2M21131747+4843554  318.323  48.7321  2M20111813+2058271  302.826  20.9742  2M03464878+2304074  56.7033  23.0687  2M19142629+1202560  288.61  12.0489  2M20204714+3702309  305.196  37.0419  2M19014937+0520105  285.456  5.33626  2M03220356+5654161  50.5148  56.9045  2M17434496−2941008  265.937  −29.6836  2M18142425−1911037  273.601  −19.1844  2M18202527−1537239  275.105  −15.6233  2M18313707−1222341  277.904  −12.3761  2M19154842+4636261  288.952  46.6073  2M19544569+4041406  298.69  40.6946  2M20000263+4529265  300.011  45.4907  2M18543899+0012432  283.662  0.212021  2M19522028+2723553  298.085  27.3987  View Large In Table A6, we list the objects with bad DR14 reductions. Table A6. Bad reductions. APOGEE ID  RA (°)  Dec. (°)  2M00354276+8619045  8.92818  86.3179  2M19315445+4813349  292.977  48.2264  2M19294950+4740246  292.456  47.6735  2M00250046+5503033  6.25194  55.0509  2M19205656+4846274  290.236  48.7743  2M19410822+4019319  295.284  40.3255  2M19325505+4746578  293.229  47.7827  2M19432504+2229419  295.854  22.495  2M06365780+0702069  99.2409  7.03526  2M19100818−0553311  287.534  −5.89197  2M07542422+3916064  118.601  39.2685  2M19193061+4842214  289.878  48.706  2M19341894+4800216  293.579  48.006  2M18315699−0100106  277.987  −1.00296  2M21223490+5110033  320.645  51.1676  2M14315024+5101159  217.959  51.0211  2M03292627+4656162  52.3595  46.9379  2M05322756+2658537  83.1149  26.9816  2M19130107−0549328  288.254  −5.82579  2M19411184+4013301  295.299  40.225  2M19455347+2412201  296.473  24.2056  2M14283924+4014496  217.164  40.2471  2M19570041+2059538  299.252  20.9983  2M19522176+1840186  298.091  18.6718  2M20464928+3411241  311.705  34.19  2M14273401+4014470  216.892  40.2464  2M20353553+5428403  308.898  54.4779  2M19343359+4823093  293.64  48.3859  2M03324489+4623388  53.1871  46.3941  2M19441693+4905154  296.071  49.0876  2M07014143+0449051  105.423  4.81809  2M21201614−0109393  320.067  −1.16092  2M08235914+0008354  125.996  0.143176  2M23583343+5635047  359.639  56.5847  2M23230618+5733020  350.776  57.5506  2M06381497+0557479  99.5624  5.96331  2M17470159−2849173  266.757  −28.8215  2M04424759+3825359  70.6983  38.4267  2M19095216+1120219  287.467  11.3394  2M17103385+3641103  257.641  36.6862  2M17192832+5804145  259.868  58.0707  2M07591385+4049311  119.808  40.8253  2M08013264+4307298  120.386  43.125  2M09095175+4254040  137.466  42.9011  2M09104765+4139238  137.699  41.6566  2M10265734+4149117  156.739  41.8199  2M10400281+4306255  160.012  43.1071  2M16011348+4149493  240.306  41.8304  2M16023049+3949503  240.627  39.8306  2M16034776+4051552  240.949  40.8653  2M16034893+4047314  240.954  40.7921  2M16042629+4030585  241.11  40.5163  2M16060267+4042385  241.511  40.7107  2M16062342+4023224  241.598  40.3896  2M16065762+4012407  241.74  40.2113  2M16103330+4146123  242.639  41.7701  2M16111200+4132006  242.8  41.5335  2M14250643+3912427  216.277  39.2119  2M14263122+3921276  216.63  39.3577  2M14264018+4018477  216.667  40.3133  2M14270892+4008013  216.787  40.1337  2M14285271+4015518  217.22  40.2644  2M06285236+0007407  97.2182  0.127981  2M13441054+2735078  206.044  27.5855  2M16280255−1306104  247.011  −13.1029  2M17181861+4206399  259.578  42.1111  2M09082892+3618428  137.121  36.3119  2M13044971+7301298  196.207  73.025  2M09294773+5544429  142.449  55.7453  2M12242677+2534571  186.112  25.5826  2M12283815+2613370  187.159  26.227  2M12284457+2553575  187.186  25.8993  2M10265302+1713099  156.721  17.2194  2M10282637+1545209  157.11  15.7558  2M21310488+1250496  322.77  12.8471  2M13500810+4233262  207.534  42.5573  2M10521368+0101300  163.057  1.02502  2M21103095+4741321  317.629  47.6923  2M18312899−0138055  277.871  −1.63487  2M18350820+0002348  278.784  0.043008  2M20510547+5125023  312.773  51.4173  2M20535239+4932004  313.468  49.5334  2M21302584+4452299  322.608  44.875  2M20321595+5337112  308.066  53.6198  2M07115139+0539169  107.964  5.65472  2M18043735+0155085  271.156  1.91904  2M20412525+3317111  310.355  33.2864  2M19532259+0424013  298.344  4.40037  2M04291231+3515567  67.3013  35.2658  2M16553254−2134100  253.886  −21.5695  2M17564183−2803554  269.174  −28.0654  2M19281906+4915086  292.079  49.2524  2M19331420+4841507  293.309  48.6974  2M19343984+4809524  293.666  48.1646  2M19094607+3747391  287.442  37.7942  2M23483899+6452355  357.162  64.8765  2M04060214+4655320  61.5089  46.9256  2M22190955−0133473  334.79  −1.56315  2M22472985+0553172  341.874  5.88812  2M14442821+4511096  221.118  45.186  2M14493515+4634280  222.396  46.5745  2M14140537+5438148  213.522  54.6375  2M23240792+5732077  351.033  57.5355  2M05060092+3556109  76.5038  35.9364  2M06202991+0723133  95.1246  7.38705  2M03353783+3140491  53.9077  31.6803  2M05350478−0443546  83.7699  −4.73184  2M21425212+6955149  325.717  69.9208  2M00474266+0351290  11.9278  3.85806  2M02275302−0855544  36.971  −8.9318  2M02310705−0758192  37.7794  −7.97201  2M02325195−0806163  38.2165  −8.10455  2M02332095−0903458  38.3373  −9.06273  2M14484064−0706253  222.169  −7.10704  2M14362130+5733384  219.089  57.5607  2M06391017+0518525  99.7924  5.3146  2M09235450+2753539  140.977  27.8983  2M09260229+2839009  141.51  28.6503  2M00525338+3832558  13.2224  38.5488  2M15122530+6658305  228.105  66.9752  2M05495923+4136264  87.4968  41.6073  2M06232278−0441150  95.845  −4.68751  2M11540771+1810106  178.532  18.1696  2M15044648+2224548  226.194  22.4152  APOGEE ID  RA (°)  Dec. (°)  2M00354276+8619045  8.92818  86.3179  2M19315445+4813349  292.977  48.2264  2M19294950+4740246  292.456  47.6735  2M00250046+5503033  6.25194  55.0509  2M19205656+4846274  290.236  48.7743  2M19410822+4019319  295.284  40.3255  2M19325505+4746578  293.229  47.7827  2M19432504+2229419  295.854  22.495  2M06365780+0702069  99.2409  7.03526  2M19100818−0553311  287.534  −5.89197  2M07542422+3916064  118.601  39.2685  2M19193061+4842214  289.878  48.706  2M19341894+4800216  293.579  48.006  2M18315699−0100106  277.987  −1.00296  2M21223490+5110033  320.645  51.1676  2M14315024+5101159  217.959  51.0211  2M03292627+4656162  52.3595  46.9379  2M05322756+2658537  83.1149  26.9816  2M19130107−0549328  288.254  −5.82579  2M19411184+4013301  295.299  40.225  2M19455347+2412201  296.473  24.2056  2M14283924+4014496  217.164  40.2471  2M19570041+2059538  299.252  20.9983  2M19522176+1840186  298.091  18.6718  2M20464928+3411241  311.705  34.19  2M14273401+4014470  216.892  40.2464  2M20353553+5428403  308.898  54.4779  2M19343359+4823093  293.64  48.3859  2M03324489+4623388  53.1871  46.3941  2M19441693+4905154  296.071  49.0876  2M07014143+0449051  105.423  4.81809  2M21201614−0109393  320.067  −1.16092  2M08235914+0008354  125.996  0.143176  2M23583343+5635047  359.639  56.5847  2M23230618+5733020  350.776  57.5506  2M06381497+0557479  99.5624  5.96331  2M17470159−2849173  266.757  −28.8215  2M04424759+3825359  70.6983  38.4267  2M19095216+1120219  287.467  11.3394  2M17103385+3641103  257.641  36.6862  2M17192832+5804145  259.868  58.0707  2M07591385+4049311  119.808  40.8253  2M08013264+4307298  120.386  43.125  2M09095175+4254040  137.466  42.9011  2M09104765+4139238  137.699  41.6566  2M10265734+4149117  156.739  41.8199  2M10400281+4306255  160.012  43.1071  2M16011348+4149493  240.306  41.8304  2M16023049+3949503  240.627  39.8306  2M16034776+4051552  240.949  40.8653  2M16034893+4047314  240.954  40.7921  2M16042629+4030585  241.11  40.5163  2M16060267+4042385  241.511  40.7107  2M16062342+4023224  241.598  40.3896  2M16065762+4012407  241.74  40.2113  2M16103330+4146123  242.639  41.7701  2M16111200+4132006  242.8  41.5335  2M14250643+3912427  216.277  39.2119  2M14263122+3921276  216.63  39.3577  2M14264018+4018477  216.667  40.3133  2M14270892+4008013  216.787  40.1337  2M14285271+4015518  217.22  40.2644  2M06285236+0007407  97.2182  0.127981  2M13441054+2735078  206.044  27.5855  2M16280255−1306104  247.011  −13.1029  2M17181861+4206399  259.578  42.1111  2M09082892+3618428  137.121  36.3119  2M13044971+7301298  196.207  73.025  2M09294773+5544429  142.449  55.7453  2M12242677+2534571  186.112  25.5826  2M12283815+2613370  187.159  26.227  2M12284457+2553575  187.186  25.8993  2M10265302+1713099  156.721  17.2194  2M10282637+1545209  157.11  15.7558  2M21310488+1250496  322.77  12.8471  2M13500810+4233262  207.534  42.5573  2M10521368+0101300  163.057  1.02502  2M21103095+4741321  317.629  47.6923  2M18312899−0138055  277.871  −1.63487  2M18350820+0002348  278.784  0.043008  2M20510547+5125023  312.773  51.4173  2M20535239+4932004  313.468  49.5334  2M21302584+4452299  322.608  44.875  2M20321595+5337112  308.066  53.6198  2M07115139+0539169  107.964  5.65472  2M18043735+0155085  271.156  1.91904  2M20412525+3317111  310.355  33.2864  2M19532259+0424013  298.344  4.40037  2M04291231+3515567  67.3013  35.2658  2M16553254−2134100  253.886  −21.5695  2M17564183−2803554  269.174  −28.0654  2M19281906+4915086  292.079  49.2524  2M19331420+4841507  293.309  48.6974  2M19343984+4809524  293.666  48.1646  2M19094607+3747391  287.442  37.7942  2M23483899+6452355  357.162  64.8765  2M04060214+4655320  61.5089  46.9256  2M22190955−0133473  334.79  −1.56315  2M22472985+0553172  341.874  5.88812  2M14442821+4511096  221.118  45.186  2M14493515+4634280  222.396  46.5745  2M14140537+5438148  213.522  54.6375  2M23240792+5732077  351.033  57.5355  2M05060092+3556109  76.5038  35.9364  2M06202991+0723133  95.1246  7.38705  2M03353783+3140491  53.9077  31.6803  2M05350478−0443546  83.7699  −4.73184  2M21425212+6955149  325.717  69.9208  2M00474266+0351290  11.9278  3.85806  2M02275302−0855544  36.971  −8.9318  2M02310705−0758192  37.7794  −7.97201  2M02325195−0806163  38.2165  −8.10455  2M02332095−0903458  38.3373  −9.06273  2M14484064−0706253  222.169  −7.10704  2M14362130+5733384  219.089  57.5607  2M06391017+0518525  99.7924  5.3146  2M09235450+2753539  140.977  27.8983  2M09260229+2839009  141.51  28.6503  2M00525338+3832558  13.2224  38.5488  2M15122530+6658305  228.105  66.9752  2M05495923+4136264  87.4968  41.6073  2M06232278−0441150  95.845  −4.68751  2M11540771+1810106  178.532  18.1696  2M15044648+2224548  226.194  22.4152  View Large © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal

Monthly Notices of the Royal Astronomical SocietyOxford University Press

Published: May 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off