TY - JOUR
AU1 - Zeng, S
AU2 - Jiménez-Serra, I
AU3 - Rivilla, V M
AU4 - Martín, S
AU5 - Martín-Pintado, J
AU6 - Requena-Torres, M A
AU7 - Armijos-Abendaño, J
AU8 - Riquelme, D
AU9 - Aladro, R
AB - ABSTRACT We present an unbiased spectral line survey towards the Galactic Centre (GC) quiescent giant molecular cloud, G+0.693 using the GBT and IRAM 30  telescopes. Our study highlights an extremely rich organic inventory of abundant amount of nitrogen (N)-bearing species in a source without signatures of star formation. We report the detection of 17 N-bearing species in this source, of which eight are complex organic molecules. A comparison of the derived abundances relative to H2 is made across various Galactic and extragalactic environments. We conclude that the unique chemistry in this source is likely to be dominated by low-velocity shocks with X-rays/cosmic rays also playing an important role in the chemistry. Like previous findings obtained for O-bearing molecules, our results for N-bearing species suggest a more efficient hydrogenation of these species on dust grains in G+0.693 than in hot cores in the Galactic disc, as a consequence of the low-dust temperatures coupled with energetic processing by X-ray/cosmic ray radiation in the GC. ISM: abundances, ISM: clouds, ISM: molecules, Galaxy: centre 1 INTRODUCTION In the past decades, interstellar molecules with increasing complexity have received significant attention due to their potential prebiotic relevance to the origin of life. These molecules can also be used as tools to constrain the physical properties of the parental environment (Calcutt et al. 2014). Up to date, nearly 200 molecules have been detected in the interstellar or circumstellar medium whilst about one-third of them are considered to be complex organic molecules (COMs).1 In astrochemistry, COMs are generally referred to carbon containing molecules with six or more atoms (Herbst & Dishoeck 2009). Across various interstellar environments, COMs are considered to be mainly associated with hot cores (e.g. Sgr B2N; Hollis, Lovas & Jewell 2000; Hollis et al. 2002, 2006; Belloche et al. 2009, 2013, 2016; Halfen, Ilyushin & Ziurys 2011, 2015; Rivilla et al. 2017) and hot corinos (e.g. IRAS 16293-2422; van Dishoeck et al. 1995; Bisschop et al. 2008; Caux et al. 2011; Jorgensen et al. 2012; Kahane et al. 2013; Jaber et al. 2014, 2017; Jørgensen et al. 2016; Ligterink et al. 2017; Lykke et al. 2017; Martín-Doménech et al. 2017) i.e. the compact hot cocoons associated with early stages of high- and low-mass star formation respectively. Apart from these star-forming regions, COMs are now routinely detected towards cold dark cloud cores (e.g. TMC-1; Ohishi & Kaifu 1998; Marcelino et al. 2009; Gratier et al. 2016; Burkhardt et al. 2017; Cordiner et al. 2017), prestellar cores (e.g. Barnard 1 (B1) and L1544; Oberg et al. 2010; Bacmann et al. 2012; Vastel et al. 2014, 2016; Jiménez-Serra et al. 2016; Quénard et al. 2017a; Vastel et al. 2018), molecular outflows (e.g. L1157; Arce et al. 2008; Codella et al. 2009, 2017; Yamaguchi et al. 2011; Lefloch et al. 2012, 2017; Mendoza et al. 2014; Mendoza et al. 2018), and photodominated regions (PDR) (Gratier et al. 2013; Cuadrado et al. 2015, 2017). Moreover, a variety of COMs have shown their existences in extreme environments such as our own Galatic Centre molecular clouds (Requena-Torres et al. 2006, 2008; Martín et al. 2008; Brünken et al. 2010; Armijos-Abendaño et al. 2014), and also towards several extragalatic nuclei (Martín et al. 2011; Aladro et al. 2015; Costagliola et al. 2015; Harada et al. 2018). Although our knowledge of the chemical complexity in the interstellar medium (ISM) has made significant progress, thanks to the current development of new instrumentation as well as laboratory experiments, chemical modelling, and spectroscopic data, the mechanisms of COMs formation from simple atoms and molecules are still subject to strong debate. Two main formation routes have been proposed to account for the presence of COMs: (i) gas phase chemistry, in which large COMs are formed via gas phase reactions from precursors such as CH3OH or H2CO, which are formed in icy grains and subsequently released to gas phase from grain surfaces (Vasyunin & Herbst 2013; Balucani, Ceccarelli & Taquet 2015; Vasyunin et al. 2017), and (ii) hydrogenation and/or radical–radical reactions on dust grain surfaces, by which COMs are formed entirely within icy mantles and subsequently released to the gas phase (Garrod & Herbst 2006; Garrod, Widicus Weaver & Herbst 2008; Taquet, Ceccarelli & Kahane 2012). The detection of numerous COMs and the study of their relative abundances in different environments of the ISM are therefore paramount to understand the emergence of molecular complexity and to constrain the main chemical routes (either in the gas phase or on grain surfaces) involved in their formation. In this regard, quiescent giant molecular clouds (QGMCs, hereafter) in the Galactic Centre (GC) offer a unique opportunity to better constrain the formation routes of COMs under extreme conditions. Within the central molecular zone (CMZ, the central 500 pc of the Milky Way; Morris & Serabyn 1996), selected observation of O-bearing molecules carried out across QGMCs in the GC has revealed that COMs are ubiquitous in the whole region (Requena-Torres et al. 2006). In comparison to hot cores in the Galactic disc, the physical conditions of the QGMCs in the GC are very different; QGMCs have high gas kinetic temperatures ranging from ∼50 to ∼120 K (Guesten et al. 1985; Hüettemeister et al. 1993; Rodríguez-Fernández et al. 2001; Ginsburg et al. 2016; Krieger et al. 2017), low-dust temperatures of ≤30 K (Rodríguez-Fernández et al. 2004), and relatively low H2 gas densities (∼104 cm−3; Rodríguez-Fernández et al. 2000). Due to the low H2 densities, the excitation temperatures of the observed COMs are also low ( ∼10–20 K; Requena-Torres et al. 2006), leading to the sub-thermal excitation of the molecules. This is in contrast with the thermal coupling between dust and gas in the Galactic disc hot cores, where the excitation temperature is close to the kinetic temperature. In order to study the origin of COMs in the GC, Requena-Torres et al. (2006) carried out a COMs survey towards several QGMCs in the CMZ and established that these clouds likely represent the largest repository of O-bearing COMs in the Galaxy. Particularly, G+0.693−0.027 (hereafter G+0.693) is one of the GC QGMCs with similar levels of chemical richness and diversity as that found in the prolific GC hot core SgrB2(N) (see e.g. Belloche et al. 2013). The low-excitation temperatures in G+0.693 help in the spectroscopic identification of large COMs since the emission peak in their spectra shifts towards longer wavelengths (≥1mm), avoiding the confusion from the emission of rotational transitions arising from lighter molecular species. Requena-Torres et al. (2008) presented the first detection of O-bearing COMs as large as that observed in the star-forming cluster SgrB2(N), such as aldehydes (glycoaldehyde CH2OHCHO, propynal HC2CHO, propenal CH2CHCHO, and propanal CH3CH2CHO) and alcohols (ethylene glycol HOCH2CH2OH and ethylene oxide c-C2H4O) with large column densities. Moreover, more exotic species such as phosphorus bearing species (PN and PO) have also been recently detected towards this source (Rivilla et al. 2018). However, the full inventory of N-bearing COMs in this cloud remained to be explored until now. Following the work of Requena-Torres et al. (2008) on O-bearing molecules, we present the chemical inventory of N-bearing COMs observed towards the quiescent GMC G+0.693 in the CMZ. In Section 2, we report the observations of a GBT 1 cm survey (discontinuous frequency covered range between  13 and 26 GHz) and of IRAM 30 m, 3 mm, 2 mm, and 1 mm surveys carried out towards this source. We provide not only a rather complete census of the N-bearing species detected, but also a comparison of their relative abundances. We also compare our results to the abundances measured across various astrophysical environments ranging from hot cores to hot corinos, molecular outflows, dark cloud cores, and also several extragalactic sources to understand the possible origin of the COM richness in G+0.693. Finally, we discuss the implications of this study on the synthesis of COMs regarding models based on gas-phase chemistry and reactions on dust grain surfaces. 2 OBSERVATIONS The spectral line surveys were performed with the IRAM 30 m telescope at Pico Veleta (Spain) and the NRAO2 100  m Robert C. Byrd Green Bank telescope (GBT) in West Virginia (USA). An overview of the observations towards the QGMC G+0.693 used in this work is presented in Table 1. The position switching mode was used in all the observations. The reference position was α(J2000.0) = 17h46m23.01s and δ(J2000.0) = −28°16′37″, obtained by Bally et al. (1987). The coordinates of the quiescent molecular cloud G+0.693−0.03 are α(J2000.0) = 17h47m22s, and δ(J2000.0) = −28°21′27″. The line intensity of our spectra is given in TA* as the molecular emission towards G+0.693 is extended over the beam (Requena-Torres et al. 2006; Martín et al. 2008; Rivilla et al. 2018). Table 1. An overview of IRAM 30 m and GBT observations towards G+0.693 used in this work. Date Frequency coverage Spectral resolution Beam size Telescope (GHz) (km s−1) (arcsec) 2009 July–2009 Oct 12–15, 18–26a 2.2–8.6 29–55 GBT 2009 July–2011 Sept 80–116 5.2–7.5 22–29 IRAM 30 m 2003 Aug–2005 Sept 128–176 6.9–9.4 14–19 IRAM 30 m 2004 July–2004 Dec 240–272 2.0 9–10 IRAM 30 m Date Frequency coverage Spectral resolution Beam size Telescope (GHz) (km s−1) (arcsec) 2009 July–2009 Oct 12–15, 18–26a 2.2–8.6 29–55 GBT 2009 July–2011 Sept 80–116 5.2–7.5 22–29 IRAM 30 m 2003 Aug–2005 Sept 128–176 6.9–9.4 14–19 IRAM 30 m 2004 July–2004 Dec 240–272 2.0 9–10 IRAM 30 m Note.a The given frequency ranges are discontinuous; the actual frequencies covered are as follows:  12.7–15.5 GHz,  18.12–18.32 GHz,  18.55–18.75 GHz,  18.85–19.06 GHz,  19.16–19.36 GHz,  19.52–19.72 GHz,  20.13–20.38 GHz,  21.36–21.54 GHz,  22.04–22.24 GHz,  23.3–24.45 GHz,  24.84–25.02 GHz, and  25.98–26.18 GHz. View Large Table 1. An overview of IRAM 30 m and GBT observations towards G+0.693 used in this work. Date Frequency coverage Spectral resolution Beam size Telescope (GHz) (km s−1) (arcsec) 2009 July–2009 Oct 12–15, 18–26a 2.2–8.6 29–55 GBT 2009 July–2011 Sept 80–116 5.2–7.5 22–29 IRAM 30 m 2003 Aug–2005 Sept 128–176 6.9–9.4 14–19 IRAM 30 m 2004 July–2004 Dec 240–272 2.0 9–10 IRAM 30 m Date Frequency coverage Spectral resolution Beam size Telescope (GHz) (km s−1) (arcsec) 2009 July–2009 Oct 12–15, 18–26a 2.2–8.6 29–55 GBT 2009 July–2011 Sept 80–116 5.2–7.5 22–29 IRAM 30 m 2003 Aug–2005 Sept 128–176 6.9–9.4 14–19 IRAM 30 m 2004 July–2004 Dec 240–272 2.0 9–10 IRAM 30 m Note.a The given frequency ranges are discontinuous; the actual frequencies covered are as follows:  12.7–15.5 GHz,  18.12–18.32 GHz,  18.55–18.75 GHz,  18.85–19.06 GHz,  19.16–19.36 GHz,  19.52–19.72 GHz,  20.13–20.38 GHz,  21.36–21.54 GHz,  22.04–22.24 GHz,  23.3–24.45 GHz,  24.84–25.02 GHz, and  25.98–26.18 GHz. View Large 2.1 IRAM 30 m single-dish observations The observations were carried out in multiple sessions between 2003 August and 2011 September. In the 2003−2005 period, our observations covered the 1 mm (240–272 GHz) and 2 mm windows (128–176 GHz) with the SIS C and D receivers using the 4  MHz filterbanks (4 MHz spectral resolution). Between 2009 and 2011 the broad-band Eight MIxer Receiver (EMIR) was used at 3 mm ( 80–116 GHz) with the Wideband Line Multiple Autocorrelator (WILMA), which provided a spectral resolution of 2 MHz. The equivalent velocity resolutions in km s−1 are summarized in Table 1, and they are high enough to resolve the typical linewidth of the emission measured towards GC QGMCs (∼20 km s−1). The half-power beam widths (HPBW) of the telescopes are in the range of 31–9 arcsec. Typical system temperatures, Tsys, ranged between  120 and 165 K,  between 180 and 390 K, and  between 220 and 680 K at 3, 2, and 1 mm, respectively. 2.2 Green Bank Telescope (GBT) observations The observations were performed in 2009 July–October. The Ku-band receiver was connected to the spectrometer, providing four 200 MHz spectral windows with a spectral resolution of 195 kHz (equivalent to a velocity resolution of  2.2–8.6 km s−1). Two polarizations were considered during the observations. We calibrated the spectra using a noise tube, providing line intensities affected by 20$$\hbox{ per cent}$$ uncertainties. 3 ANALYSIS AND RESULTS 3.1 Line identification and molecular column densities For line identification and analysis, our spectra were exported from the gildas/class software package3 to the madcuba package.4madcuba contains molecular data bases such as Jet Propulsion Laboratory (JPL)5 (Pickett et al. 1998) and The Cologne Database for Molecular Spectroscopy (CDMS)6 (Müller et al. 2001, 2005; Endres et al. 2016) that provide the frequencies and the spectroscopic information of different species. With the identified transitions, the spectral line identification and modelling (SLIM) package implemented within madcuba was used to produce synthetic spectra by assuming local thermodynamical equilibrium (LTE) conditions and considering the effect of line opacity. These synthetic spectra were compared to the observed spectra where the parameters of total molecular column density, Ntot, excitation temperature, Tex, peak velocity, VLSR, and linewidth, Δν of the emission, are fit. Then, the madcuba-autofit tool was able to provide the best non-linear least-squared fit using the Levenberg–Marquardt algorithm. A representative sample of the observed and best Guassian fitted spectra for several molecular transitions observed in G+0.693 is shown in Fig. 1, and the complete set of spectra for detected molecular lines is available as supplementary material (Figs B1–B17). The physical parameters, together with their associated uncertainties derived from the madcuba autofit, are listed in Table 2. Note that for some molecules such as ortho-H2CCN and HC7N, the autofit algorithm would only converge if the values of VLSR and/or Δν were fixed (see the corresponding values in Table 2). Since these parameters are fixed to a certain value by the user, no uncertainties are associated with VLSR and/or Δν within madcuba during the line fitting process. In addition, Tex of a given species can only be constrained if multiple rotational transitions are available; otherwise, Tex needs to be fixed as well. Figure 1. View largeDownload slide Sample line profiles of a number of detected N-bearing molecules in G+0.693. The LTE best fits from madcuba are shown in red lines. Figure 1. View largeDownload slide Sample line profiles of a number of detected N-bearing molecules in G+0.693. The LTE best fits from madcuba are shown in red lines. Table 2. Physical parameters of the N-bearing molecules derived from the madcuba–LTE analysis of molecular spectra for G+0.693. Formula Tex VLSR Δν Ntot (K) (km s−1) (km s−1) (× 1013 cm−2) NO 4.1 ± 0.4 68 ± 1 21 ± 1 3600 ± 200 HNCO, K,a=0 17 ± 1 67 ± 1 23 ± 1 320 ± 11 HNCO, Ka=1 29 ± 2 68 ± 1 22 ± 1 16 ± 1 HNCOa – – – 336 ± 12 HOCN 8.1 ± 0.3 69 ± 1 20±1 1.9 ± 0.1 C3N 7.3 ± 1.5 68 ± 1 18±2 4.3 ± 0.2 CH2NH 9.7 ± 0.4 69 ± 1 25±1 54 ± 3 H13CCCN 12 ± 2 68 ± 1 22±1 3.4 ± 0.6 HC3Nb – – – 71 ± 13 HCCNC 7.0 ± 2.1 71 ± 1 19 ± 2 2.3 ± 0.7 o-H2CCNc 12 ± 1 70 30 5.4 ± 0.3 p-H2CCN 9.1 ± 0.6 70 ± 1 21 ± 1 17 ± 2 (o+p)-H2CCNd – – – 22 ± 2 o-NH2CN 6.3 ± 0.3 66 ± 1 24 ± 1 3.8 ± 0.2 p-NH2CN 6.8 ± 0.2 67 ± 1 24 ± 1 27 ± 2 (o+p)-NH2CNd – – – 31 ± 2 CH3CN(J = 5–4) 140 ± 26 69 ± 1 27 ± 1 100 ± 22 CH3CN(J = 6–5) 100 ± 8 69 ± 1 25 ± 1 46 ± 5 CH3CN(J = 7–6) 78 ± 6 69 ± 1 26 ± 1 27 ± 3 CH3CN(J = 8–7) 73 ± 6 68 ± 1 26 ± 1 15 ± 1 CH3CN(J = 9–8) 85 ± 11 68 ± 1 25 ± 1 13 ± 2 CH3CN(J = 14–13) 83 ± 9 70 ± 1 23 ± 1 0.9 ± 0.1 CH3CN(J = 15–14)c 130 71 ± 1 16 ± 2 0.40 ± 0.04 CH3CNe 14 71 21 11.5 ± 0.3 a-NH2CHO 9.8 ± 0.6 71 ± 1 23 ± 1 5.5 ± 0.3 b-NH2CHO 3.7 ± 0.7 68 ± 1 23 ± 2 57 ± 14 NH2CHOf 63 ± 14 HC5N 16.2 ± 0.2 67 ± 1 22 ± 1 26 ± 1 C2H3CN 10.8 ± 1.1 68 ± 1 22 ± 2 9 ± 1 CH3NH2 16.2 ± 0.8 67 ± 1 20 ± 1 30 ± 9 CH3NCO 7.9 ± 0.4 67 ± 1 23 ± 2 6.6 ± 0.4 C2H5CN 17.7 ± 1.6 69 ± 1 24 ± 2 4.1 ± 0.4 HC7N 7 ± 1 66 26 ± 2 1.5 ± 0.3 Tentative detections HNCCC 5.3 ± 0.5 68 ± 1 14 ± 3 ≤0.2 ±0.1 CH3C3Nc 15 68 20 ≤4.5 ±0.3 Formula Tex VLSR Δν Ntot (K) (km s−1) (km s−1) (× 1013 cm−2) NO 4.1 ± 0.4 68 ± 1 21 ± 1 3600 ± 200 HNCO, K,a=0 17 ± 1 67 ± 1 23 ± 1 320 ± 11 HNCO, Ka=1 29 ± 2 68 ± 1 22 ± 1 16 ± 1 HNCOa – – – 336 ± 12 HOCN 8.1 ± 0.3 69 ± 1 20±1 1.9 ± 0.1 C3N 7.3 ± 1.5 68 ± 1 18±2 4.3 ± 0.2 CH2NH 9.7 ± 0.4 69 ± 1 25±1 54 ± 3 H13CCCN 12 ± 2 68 ± 1 22±1 3.4 ± 0.6 HC3Nb – – – 71 ± 13 HCCNC 7.0 ± 2.1 71 ± 1 19 ± 2 2.3 ± 0.7 o-H2CCNc 12 ± 1 70 30 5.4 ± 0.3 p-H2CCN 9.1 ± 0.6 70 ± 1 21 ± 1 17 ± 2 (o+p)-H2CCNd – – – 22 ± 2 o-NH2CN 6.3 ± 0.3 66 ± 1 24 ± 1 3.8 ± 0.2 p-NH2CN 6.8 ± 0.2 67 ± 1 24 ± 1 27 ± 2 (o+p)-NH2CNd – – – 31 ± 2 CH3CN(J = 5–4) 140 ± 26 69 ± 1 27 ± 1 100 ± 22 CH3CN(J = 6–5) 100 ± 8 69 ± 1 25 ± 1 46 ± 5 CH3CN(J = 7–6) 78 ± 6 69 ± 1 26 ± 1 27 ± 3 CH3CN(J = 8–7) 73 ± 6 68 ± 1 26 ± 1 15 ± 1 CH3CN(J = 9–8) 85 ± 11 68 ± 1 25 ± 1 13 ± 2 CH3CN(J = 14–13) 83 ± 9 70 ± 1 23 ± 1 0.9 ± 0.1 CH3CN(J = 15–14)c 130 71 ± 1 16 ± 2 0.40 ± 0.04 CH3CNe 14 71 21 11.5 ± 0.3 a-NH2CHO 9.8 ± 0.6 71 ± 1 23 ± 1 5.5 ± 0.3 b-NH2CHO 3.7 ± 0.7 68 ± 1 23 ± 2 57 ± 14 NH2CHOf 63 ± 14 HC5N 16.2 ± 0.2 67 ± 1 22 ± 1 26 ± 1 C2H3CN 10.8 ± 1.1 68 ± 1 22 ± 2 9 ± 1 CH3NH2 16.2 ± 0.8 67 ± 1 20 ± 1 30 ± 9 CH3NCO 7.9 ± 0.4 67 ± 1 23 ± 2 6.6 ± 0.4 C2H5CN 17.7 ± 1.6 69 ± 1 24 ± 2 4.1 ± 0.4 HC7N 7 ± 1 66 26 ± 2 1.5 ± 0.3 Tentative detections HNCCC 5.3 ± 0.5 68 ± 1 14 ± 3 ≤0.2 ±0.1 CH3C3Nc 15 68 20 ≤4.5 ±0.3 Note.aNtot calculated by the sum of K,a=0 and Ka=1 rotational ladder. bNtot was derived from its isotopologue; H13CCCN due to the emission lines of HC3N are optically thick. We adapted the 12C/13C ∼ 21 from Armijos-Abendaño et al. (2014) in G+0.693. c Values fixed. dNtot calculated by the sum of the ortho and para species. eTex and Ntot derived from rotational diagram analysis (see Fig. 3). fNtot calculated by the sum of a-type and b-type transitions. View Large Table 2. Physical parameters of the N-bearing molecules derived from the madcuba–LTE analysis of molecular spectra for G+0.693. Formula Tex VLSR Δν Ntot (K) (km s−1) (km s−1) (× 1013 cm−2) NO 4.1 ± 0.4 68 ± 1 21 ± 1 3600 ± 200 HNCO, K,a=0 17 ± 1 67 ± 1 23 ± 1 320 ± 11 HNCO, Ka=1 29 ± 2 68 ± 1 22 ± 1 16 ± 1 HNCOa – – – 336 ± 12 HOCN 8.1 ± 0.3 69 ± 1 20±1 1.9 ± 0.1 C3N 7.3 ± 1.5 68 ± 1 18±2 4.3 ± 0.2 CH2NH 9.7 ± 0.4 69 ± 1 25±1 54 ± 3 H13CCCN 12 ± 2 68 ± 1 22±1 3.4 ± 0.6 HC3Nb – – – 71 ± 13 HCCNC 7.0 ± 2.1 71 ± 1 19 ± 2 2.3 ± 0.7 o-H2CCNc 12 ± 1 70 30 5.4 ± 0.3 p-H2CCN 9.1 ± 0.6 70 ± 1 21 ± 1 17 ± 2 (o+p)-H2CCNd – – – 22 ± 2 o-NH2CN 6.3 ± 0.3 66 ± 1 24 ± 1 3.8 ± 0.2 p-NH2CN 6.8 ± 0.2 67 ± 1 24 ± 1 27 ± 2 (o+p)-NH2CNd – – – 31 ± 2 CH3CN(J = 5–4) 140 ± 26 69 ± 1 27 ± 1 100 ± 22 CH3CN(J = 6–5) 100 ± 8 69 ± 1 25 ± 1 46 ± 5 CH3CN(J = 7–6) 78 ± 6 69 ± 1 26 ± 1 27 ± 3 CH3CN(J = 8–7) 73 ± 6 68 ± 1 26 ± 1 15 ± 1 CH3CN(J = 9–8) 85 ± 11 68 ± 1 25 ± 1 13 ± 2 CH3CN(J = 14–13) 83 ± 9 70 ± 1 23 ± 1 0.9 ± 0.1 CH3CN(J = 15–14)c 130 71 ± 1 16 ± 2 0.40 ± 0.04 CH3CNe 14 71 21 11.5 ± 0.3 a-NH2CHO 9.8 ± 0.6 71 ± 1 23 ± 1 5.5 ± 0.3 b-NH2CHO 3.7 ± 0.7 68 ± 1 23 ± 2 57 ± 14 NH2CHOf 63 ± 14 HC5N 16.2 ± 0.2 67 ± 1 22 ± 1 26 ± 1 C2H3CN 10.8 ± 1.1 68 ± 1 22 ± 2 9 ± 1 CH3NH2 16.2 ± 0.8 67 ± 1 20 ± 1 30 ± 9 CH3NCO 7.9 ± 0.4 67 ± 1 23 ± 2 6.6 ± 0.4 C2H5CN 17.7 ± 1.6 69 ± 1 24 ± 2 4.1 ± 0.4 HC7N 7 ± 1 66 26 ± 2 1.5 ± 0.3 Tentative detections HNCCC 5.3 ± 0.5 68 ± 1 14 ± 3 ≤0.2 ±0.1 CH3C3Nc 15 68 20 ≤4.5 ±0.3 Formula Tex VLSR Δν Ntot (K) (km s−1) (km s−1) (× 1013 cm−2) NO 4.1 ± 0.4 68 ± 1 21 ± 1 3600 ± 200 HNCO, K,a=0 17 ± 1 67 ± 1 23 ± 1 320 ± 11 HNCO, Ka=1 29 ± 2 68 ± 1 22 ± 1 16 ± 1 HNCOa – – – 336 ± 12 HOCN 8.1 ± 0.3 69 ± 1 20±1 1.9 ± 0.1 C3N 7.3 ± 1.5 68 ± 1 18±2 4.3 ± 0.2 CH2NH 9.7 ± 0.4 69 ± 1 25±1 54 ± 3 H13CCCN 12 ± 2 68 ± 1 22±1 3.4 ± 0.6 HC3Nb – – – 71 ± 13 HCCNC 7.0 ± 2.1 71 ± 1 19 ± 2 2.3 ± 0.7 o-H2CCNc 12 ± 1 70 30 5.4 ± 0.3 p-H2CCN 9.1 ± 0.6 70 ± 1 21 ± 1 17 ± 2 (o+p)-H2CCNd – – – 22 ± 2 o-NH2CN 6.3 ± 0.3 66 ± 1 24 ± 1 3.8 ± 0.2 p-NH2CN 6.8 ± 0.2 67 ± 1 24 ± 1 27 ± 2 (o+p)-NH2CNd – – – 31 ± 2 CH3CN(J = 5–4) 140 ± 26 69 ± 1 27 ± 1 100 ± 22 CH3CN(J = 6–5) 100 ± 8 69 ± 1 25 ± 1 46 ± 5 CH3CN(J = 7–6) 78 ± 6 69 ± 1 26 ± 1 27 ± 3 CH3CN(J = 8–7) 73 ± 6 68 ± 1 26 ± 1 15 ± 1 CH3CN(J = 9–8) 85 ± 11 68 ± 1 25 ± 1 13 ± 2 CH3CN(J = 14–13) 83 ± 9 70 ± 1 23 ± 1 0.9 ± 0.1 CH3CN(J = 15–14)c 130 71 ± 1 16 ± 2 0.40 ± 0.04 CH3CNe 14 71 21 11.5 ± 0.3 a-NH2CHO 9.8 ± 0.6 71 ± 1 23 ± 1 5.5 ± 0.3 b-NH2CHO 3.7 ± 0.7 68 ± 1 23 ± 2 57 ± 14 NH2CHOf 63 ± 14 HC5N 16.2 ± 0.2 67 ± 1 22 ± 1 26 ± 1 C2H3CN 10.8 ± 1.1 68 ± 1 22 ± 2 9 ± 1 CH3NH2 16.2 ± 0.8 67 ± 1 20 ± 1 30 ± 9 CH3NCO 7.9 ± 0.4 67 ± 1 23 ± 2 6.6 ± 0.4 C2H5CN 17.7 ± 1.6 69 ± 1 24 ± 2 4.1 ± 0.4 HC7N 7 ± 1 66 26 ± 2 1.5 ± 0.3 Tentative detections HNCCC 5.3 ± 0.5 68 ± 1 14 ± 3 ≤0.2 ±0.1 CH3C3Nc 15 68 20 ≤4.5 ±0.3 Note.aNtot calculated by the sum of K,a=0 and Ka=1 rotational ladder. bNtot was derived from its isotopologue; H13CCCN due to the emission lines of HC3N are optically thick. We adapted the 12C/13C ∼ 21 from Armijos-Abendaño et al. (2014) in G+0.693. c Values fixed. dNtot calculated by the sum of the ortho and para species. eTex and Ntot derived from rotational diagram analysis (see Fig. 3). fNtot calculated by the sum of a-type and b-type transitions. View Large As shown in Table 2, the Ntot of some molecules are attributed to different types of transitions: a- or b-type transitions for formamide (NH2CHO), ortho/para transitions for cyanomethyl radical (H2CCN) and cyanamide (NH2CN), and K ladders/K,a ladders present in methyl cyanide (CH3CN) and isocyanic acid (HNCO), respectively. In particular, rotational diagram analysis is used to estimate the excitation temperature and column density of CH3CN due to the presence of several series of K-ladder transitions (more details in Section 3.3). The main spectroscopic information for these molecules is summarized in Appendix A1. In total, we have identified 19 N-bearing molecules excluding their isotopologues towards G+0.693. Among them, there are 17 clear detections with peak intensities above 3σ, and 2 tentative detections (where only one or two transitions showed peak intensities higher than 3σ; see Fig. 2). For the non-detections, we estimated 3σ integrated line intensity noise levels as 3 × rms $$\times \sqrt{\Delta \nu \times \delta v}$$, where rms (in K) is estimated with madcuba, Δν is the assumed linewidth of the  transition (20 km s−1), and δv is the velocity spectral resolution in km s−1. The 3σ column densities and corresponding relative abundances with respect to H2 assuming Tex =  15–20 K are listed in Table 3. Figure 2. View largeDownload slide Line profiles are tentative detections of molecules HNCCC and CH3C3N. The LTE best fits from madcuba are shown in red lines. Figure 2. View largeDownload slide Line profiles are tentative detections of molecules HNCCC and CH3C3N. The LTE best fits from madcuba are shown in red lines. Table 3. Upper limits for undetected N-bearing species. Formula Ntot Abundance (× 1013 cm−2) (× 10−11) C2N ≤0.3 ≤1.9 NCO ≤2 ≤11.7 H2CN ≤0.3 ≤1.9 HCCN ≤1.1 ≤8.2 HCNO ≤0.3 ≤2.5 HONC ≤0.1 ≤0.7 C4N ≤0.4 ≤2.9 HCOCN ≤6 ≤44.7 NH2OH ≤2 ≤14.1 l-HC4N ≤1 ≤7.4 C5N ≤0.6 ≤4.7 CH3NC ≤0.3 ≤2.3 (NH2)2CO ≤1 ≤6.8 C2H3NC ≤0.3 ≤2.2 CH2(CN)2 ≤2.5 ≤18.6 CH3OCN ≤0.6 ≤4.7 CH3CNO ≤0.1 ≤0.8 NCCONH2 ≤0.3 ≤2.5 l-HC6N ≤0.3 ≤2.3 CH3CCNC ≤0.3 ≤2.3 H2CCCHCN ≤2 ≤14.8 H2NCH2CN ≤0.6 ≤4.7 C2H3NH2 ≤4 ≤29.5 c-C2H4NH ≤4 ≤29.5 CH3C5N ≤0.1 ≤0.9 HC9N ≤0.4 ≤2.6 NH2CH2CH2OH ≤1 ≤8.9 i-C3H7CN ≤0.6 ≤4.7 n-C3H7CN ≤1.1 ≤8.2 HC11N ≤0.2 ≤1.9 HC13N ≤1 ≤1.3 Formula Ntot Abundance (× 1013 cm−2) (× 10−11) C2N ≤0.3 ≤1.9 NCO ≤2 ≤11.7 H2CN ≤0.3 ≤1.9 HCCN ≤1.1 ≤8.2 HCNO ≤0.3 ≤2.5 HONC ≤0.1 ≤0.7 C4N ≤0.4 ≤2.9 HCOCN ≤6 ≤44.7 NH2OH ≤2 ≤14.1 l-HC4N ≤1 ≤7.4 C5N ≤0.6 ≤4.7 CH3NC ≤0.3 ≤2.3 (NH2)2CO ≤1 ≤6.8 C2H3NC ≤0.3 ≤2.2 CH2(CN)2 ≤2.5 ≤18.6 CH3OCN ≤0.6 ≤4.7 CH3CNO ≤0.1 ≤0.8 NCCONH2 ≤0.3 ≤2.5 l-HC6N ≤0.3 ≤2.3 CH3CCNC ≤0.3 ≤2.3 H2CCCHCN ≤2 ≤14.8 H2NCH2CN ≤0.6 ≤4.7 C2H3NH2 ≤4 ≤29.5 c-C2H4NH ≤4 ≤29.5 CH3C5N ≤0.1 ≤0.9 HC9N ≤0.4 ≤2.6 NH2CH2CH2OH ≤1 ≤8.9 i-C3H7CN ≤0.6 ≤4.7 n-C3H7CN ≤1.1 ≤8.2 HC11N ≤0.2 ≤1.9 HC13N ≤1 ≤1.3 View Large Table 3. Upper limits for undetected N-bearing species. Formula Ntot Abundance (× 1013 cm−2) (× 10−11) C2N ≤0.3 ≤1.9 NCO ≤2 ≤11.7 H2CN ≤0.3 ≤1.9 HCCN ≤1.1 ≤8.2 HCNO ≤0.3 ≤2.5 HONC ≤0.1 ≤0.7 C4N ≤0.4 ≤2.9 HCOCN ≤6 ≤44.7 NH2OH ≤2 ≤14.1 l-HC4N ≤1 ≤7.4 C5N ≤0.6 ≤4.7 CH3NC ≤0.3 ≤2.3 (NH2)2CO ≤1 ≤6.8 C2H3NC ≤0.3 ≤2.2 CH2(CN)2 ≤2.5 ≤18.6 CH3OCN ≤0.6 ≤4.7 CH3CNO ≤0.1 ≤0.8 NCCONH2 ≤0.3 ≤2.5 l-HC6N ≤0.3 ≤2.3 CH3CCNC ≤0.3 ≤2.3 H2CCCHCN ≤2 ≤14.8 H2NCH2CN ≤0.6 ≤4.7 C2H3NH2 ≤4 ≤29.5 c-C2H4NH ≤4 ≤29.5 CH3C5N ≤0.1 ≤0.9 HC9N ≤0.4 ≤2.6 NH2CH2CH2OH ≤1 ≤8.9 i-C3H7CN ≤0.6 ≤4.7 n-C3H7CN ≤1.1 ≤8.2 HC11N ≤0.2 ≤1.9 HC13N ≤1 ≤1.3 Formula Ntot Abundance (× 1013 cm−2) (× 10−11) C2N ≤0.3 ≤1.9 NCO ≤2 ≤11.7 H2CN ≤0.3 ≤1.9 HCCN ≤1.1 ≤8.2 HCNO ≤0.3 ≤2.5 HONC ≤0.1 ≤0.7 C4N ≤0.4 ≤2.9 HCOCN ≤6 ≤44.7 NH2OH ≤2 ≤14.1 l-HC4N ≤1 ≤7.4 C5N ≤0.6 ≤4.7 CH3NC ≤0.3 ≤2.3 (NH2)2CO ≤1 ≤6.8 C2H3NC ≤0.3 ≤2.2 CH2(CN)2 ≤2.5 ≤18.6 CH3OCN ≤0.6 ≤4.7 CH3CNO ≤0.1 ≤0.8 NCCONH2 ≤0.3 ≤2.5 l-HC6N ≤0.3 ≤2.3 CH3CCNC ≤0.3 ≤2.3 H2CCCHCN ≤2 ≤14.8 H2NCH2CN ≤0.6 ≤4.7 C2H3NH2 ≤4 ≤29.5 c-C2H4NH ≤4 ≤29.5 CH3C5N ≤0.1 ≤0.9 HC9N ≤0.4 ≤2.6 NH2CH2CH2OH ≤1 ≤8.9 i-C3H7CN ≤0.6 ≤4.7 n-C3H7CN ≤1.1 ≤8.2 HC11N ≤0.2 ≤1.9 HC13N ≤1 ≤1.3 View Large 3.2 Rotational diagram analysis for CH3CN CH3CN is a symmetric rotor in which its rotational energy levels (or J transitions) are further divided into successive Kcomponents. Consequently, spectral lines due to different K values are shifted in frequency with respect to each other, giving rise to a so-called ‘K ladder’ spectrum. Due to the fact that radiative transitions are forbidden between K-ladders, the level populations are expected to be thermalized, and therefore determined by collisional excitation within K ladder. Then Tex of the Kladder levels for the J + 1→J transition of CH3CN provides a measurement of Tkin of the source. In our survey, the K-ladders of J = 5→4, J = 6→5, J = 7→6, J = 8→7, J = 9→8, J = 14→13, and J = 15→14 are observed for CH3CN in G+0.693. The individual Tex obtained from theseK ladders lies between   73 and 140 K, which indicates that Tkin can be up to 140 K. These values are consistent with the gas kinetic temperatures Tkin ∼ 50–120 K previously reported by Guesten et al. (1985), Hüettemeister et al. (1993), and Krieger et al. (2017) using another symmetric top molecule, ammonia (NH3). In addition, a rotational diagram was constructed for CH3CN to derive the overall Ntot and Trot (see Fig. 3). A linear regression line was fitted to the lines with the lowest K values i.e. K = 0, 1, and 2 for all J + 1→J transitions, yielding Trot =  15 ± 1 K. By fixing Trot= 15 K, VLSR= 71 km s−1, and  Δν = 21 km s−1 in madcuba, the autofit algorithm converged to give a Ntot(CH3CN) value of (1.15 ± 0.03) × 1014 cm−2. Figure 3. View largeDownload slide CH3CN rotational diagram constructed using the K = 0, 1, and 2 transitions for all detected J+1→J transitions. The blue straight line indicates the best linear regression fit to the data points. Figure 3. View largeDownload slide CH3CN rotational diagram constructed using the K = 0, 1, and 2 transitions for all detected J+1→J transitions. The blue straight line indicates the best linear regression fit to the data points. 3.3 Excitation temperature versus gas kinetic temperature Tex derived from the source using different molecules ranges between   9 and 30 K. Note that Trot =  15 ± 1 K extracted from the rotational diagram of CHCN3 also falls in the same range. This range of Tex is consistent with those found in Requena-Torres et al. (2006, 2008), and significantly lower than the derived kinetic temperatures of the gas (Tkin=  73–140 K; see Section 3.2). This implies that the molecular emission in this cloud is sub-thermally excited due to its relatively low H2 gas densities (see Rodríguez-Fernández et al. 2000). Overall, from the detected N-bearing molecules, we derive a range of Tex and Tkin that are consistent with previous studies. They both strengthen the fact that G+0.693 indeed has a much lower Tex compared to its Tkin, a signature of the sub-thermal excitation of molecules within the cloud. 3.4 Molecular abundances relative to H2, and parent molecule of each functional group Since we know that the emission is extended, the relative abundances can be estimated by dividing the measured molecular column densities by the H2 column density ($$N_{\rm H_2}$$). In this study, we adopt the H2 column density $$N_{\rm H_2}$$ = 1.35 × 1023 cm−2 derived by Martín et al. (2008) from C18O. In our calculations, we assume that all molecules present a similar spatial distribution to C18O when determining the observed abundances (i.e. all molecules arise from the same region). For molecules with clear detections and tentative detections, high abundances relative to H2 are observed towards G+0.693, ranging from 10−11 to 10−8. Uncertainties in the relative abundances can be estimated by propagating errors in the molecular column densities provided by the madcuba autofit algorithm. A summary of the calculated abundances relative to H2 is given in the second column of Tables 4, 5, and 6. In order to explore the possible differences in chemistry, in Tables 4, 5, and 6, we also report the molecular abundance ratios between each molecule and their corresponding ‘parent’ species within each functional group: CH3CN for nitriles (–CN), CH3NH2 for amines (–NH), and HNCO for cyanates (–NCO). Note that we refer here to parent species as the most abundant molecules of each functional group. Table 4. Abundances ratios with respect to H2 and CH3CN. Molecule X(H2) X(CH3CN) (× 10−10) CH3CN 9 1 H2CCN 16 2 C3N 3 0.4 HCCCN 54 6 HCCNC 2 0.2 HNCCC ≤0.1 ≤0.02 C2H3CN 7 0.8 C2H5CN 3 0.4 CH3C3N ≤3 ≤0.4 HC5N 19 2.21 HC7N 1 0.1 NH2CNa 23 3 Molecule X(H2) X(CH3CN) (× 10−10) CH3CN 9 1 H2CCN 16 2 C3N 3 0.4 HCCCN 54 6 HCCNC 2 0.2 HNCCC ≤0.1 ≤0.02 C2H3CN 7 0.8 C2H5CN 3 0.4 CH3C3N ≤3 ≤0.4 HC5N 19 2.21 HC7N 1 0.1 NH2CNa 23 3 Note. a Molecule that potentially contains two functional groups, i.e. NH2CN contains both NH and CN group. View Large Table 4. Abundances ratios with respect to H2 and CH3CN. Molecule X(H2) X(CH3CN) (× 10−10) CH3CN 9 1 H2CCN 16 2 C3N 3 0.4 HCCCN 54 6 HCCNC 2 0.2 HNCCC ≤0.1 ≤0.02 C2H3CN 7 0.8 C2H5CN 3 0.4 CH3C3N ≤3 ≤0.4 HC5N 19 2.21 HC7N 1 0.1 NH2CNa 23 3 Molecule X(H2) X(CH3CN) (× 10−10) CH3CN 9 1 H2CCN 16 2 C3N 3 0.4 HCCCN 54 6 HCCNC 2 0.2 HNCCC ≤0.1 ≤0.02 C2H3CN 7 0.8 C2H5CN 3 0.4 CH3C3N ≤3 ≤0.4 HC5N 19 2.21 HC7N 1 0.1 NH2CNa 23 3 Note. a Molecule that potentially contains two functional groups, i.e. NH2CN contains both NH and CN group. View Large Table 5. Abundances ratios with respect to H2 and CH3NH2. Molecule X(H2) X(CH3NH2) (× 10−10) CH3NH2 221 1 CH2NH 43 0.2 NH2CNa 23 0.1 NH2CHOa 47 0.2 Molecule X(H2) X(CH3NH2) (× 10−10) CH3NH2 221 1 CH2NH 43 0.2 NH2CNa 23 0.1 NH2CHOa 47 0.2 Note. aMolecules that potentially contain two functional groups, i.e. NH2CN contains both NH and CN group whilst NH2CHO contains both NH and NCOgroup. View Large Table 5. Abundances ratios with respect to H2 and CH3NH2. Molecule X(H2) X(CH3NH2) (× 10−10) CH3NH2 221 1 CH2NH 43 0.2 NH2CNa 23 0.1 NH2CHOa 47 0.2 Molecule X(H2) X(CH3NH2) (× 10−10) CH3NH2 221 1 CH2NH 43 0.2 NH2CNa 23 0.1 NH2CHOa 47 0.2 Note. aMolecules that potentially contain two functional groups, i.e. NH2CN contains both NH and CN group whilst NH2CHO contains both NH and NCOgroup. View Large Table 6. Abundances ratios with respect to H2 and HNCO. Molecule X(H2) X(HNCO) (× 10−10) HNCO 249 1 HOCN 1 0.01 NH2CHOa 47 0.2 CH3NCO 5 0.02 Molecule X(H2) X(HNCO) (× 10−10) HNCO 249 1 HOCN 1 0.01 NH2CHOa 47 0.2 CH3NCO 5 0.02 Note. aMolecules that potentially contain two functional groups, i.e. NH2CHO contains both NH and NCO group. View Large Table 6. Abundances ratios with respect to H2 and HNCO. Molecule X(H2) X(HNCO) (× 10−10) HNCO 249 1 HOCN 1 0.01 NH2CHOa 47 0.2 CH3NCO 5 0.02 Molecule X(H2) X(HNCO) (× 10−10) HNCO 249 1 HOCN 1 0.01 NH2CHOa 47 0.2 CH3NCO 5 0.02 Note. aMolecules that potentially contain two functional groups, i.e. NH2CHO contains both NH and NCO group. View Large 4 DISCUSSION As for the O-bearing family in Requena-Torres et al. (2008), the full census of the identified N-bearing molecules in G+0.693 is presented in Figs 5 and 6. The purpose of these diagrams is to organize N-bearing molecules by increasing complexity (increasing the number of carbon C, nitrogen N, and oxygen O atoms) so that the possible relation between two molecules, or even from a simple molecule to a very complex one, can be quickly visualized in one diagram. Besides molecules, intermediate species such as radicals and chemically unstable species, which presumably represent the missing links between chemical reactions are also included in the diagram. Molecules that have not been identified in space either due to lack of spectroscopic data or due to lack of sensitivity in observations are also indicated. Both the measured molecular abundances with respect to H2 and the derived upper limits for the non-detections are given in the diagrams. These diagrams could be used as guidance for future searches of species as well as for spectroscopic laboratory experiments. 4.1 Comparison of relative abundances across different Galactic environments It has been proposed that the formation processes of COMs in hot and cold environments largely differ. In hot cores and hot corinos, COMs are believed to form via radical–radical association on the surface of dust grains as radicals become mobile at temperatures ≥30 K during the warming up of the protostellar envelope (Garrod et al. 2008). The heating from the protostar sublimates the ices (and its content in COMs) off dust grains, triggering a complex chemistry in the hot envelope. In contrast to these hot environments, the gas temperature in cold dark cloud cores rarely exceeds 15 K, and therefore the chemistry is dominated by ion–molecule reactions in the gas phase since the gas radical mobility necessary for COM formation on grains does not occur. As a result of low-gas temperature, highly reactive species, including ions, radicals, and unsaturated species such as cyanopolyynes HCnN are abundant in cold core sources (Ohishi & Kaifu 1998; Smith, Herbst & Chang 2004). COMs have also been found in shocked regions, where the gas is enriched by ice sputtering (Arce et al. 2008) and/or affected by gas-phase chemistry (Codella et al. 2017). The origin of the formation of COMs in G+0.693 has been rather elusive due to its peculiar physical conditions compared to other environments. The ejection of COMs from grain mantles in G+0.693 has been proposed to be due to non-dissociative shocks with velocities ≤40 km s−1 (Requena-Torres et al. 2006, 2008; Martín et al. 2008). This seems to be consistent with the typical line widths (∼20 km−1) derived from the molecular transitions. In order to better understand the formation mechanisms of COMs in G+0.693, we compared the derived abundances relative to H2 of the detected N-bearing molecules across different astronomical environments. These sources include the hot cores Sgr B2(N) and Orion KL, the hot corino IRAS16293−2422 (IRAS16293 hereafter), the dark cloud TMC-1, and the shocked region L1157-B1. As illustrated in Fig. 4, the abundances relative to H2 are plotted for detected and tentatively detected species, which can be categorized into four groups: (1) cyanopolyynes HCnN, (2) nitriles –CN, (3) amines –NH, and (4) cyanates –NCO. Within each group, species are organized by increasing complexity i.e. increase the number of C, O, and N from left to right. For completeness, the abundance ratios between relative molecules such as isomers and ‘parents/daughters’ species are listed in Table 7. Figure 4. View largeDownload slide Molecular abundances relative to H2 in different sources. Blue triangles represent the abundances derived from this work towards G+0.693; magenta stars are the results from L1157−B1 (Arce et al. 2008; Mendoza et al. 2014; Mendoza et al. 2018); red circle are the results from Sgr B2N (Ziurys et al. 1991; Belloche et al. 2013; Cernicharo et al. 2016); purple circle represents the averaged results from Orion KL hot core (Blake et al. 1987; Sutton et al. 1995); orange pentagon and green rectangle represent values obtained from TMC-1 and IRAS16293, respectively (Gerin, Viala & Casoli 1993; Jaber et al. 2014, 2017; Gratier et al. 2016; Coutens et al. 2017b; Martín-Doménech et al. 2017). Figure 4. View largeDownload slide Molecular abundances relative to H2 in different sources. Blue triangles represent the abundances derived from this work towards G+0.693; magenta stars are the results from L1157−B1 (Arce et al. 2008; Mendoza et al. 2014; Mendoza et al. 2018); red circle are the results from Sgr B2N (Ziurys et al. 1991; Belloche et al. 2013; Cernicharo et al. 2016); purple circle represents the averaged results from Orion KL hot core (Blake et al. 1987; Sutton et al. 1995); orange pentagon and green rectangle represent values obtained from TMC-1 and IRAS16293, respectively (Gerin, Viala & Casoli 1993; Jaber et al. 2014, 2017; Gratier et al. 2016; Coutens et al. 2017b; Martín-Doménech et al. 2017). Figure 5. View largeDownload slide Chemical diagram of N-bearing species to illustrate the relationship between each individual species. Complexity increases from right to left by adding C (four areas, C, C–C, C–C–C, and n–C divided by vertical line), from top to bottom by adding H, and addition of N is indicated in diagonal direction. Molecules are encapsulated in square boxes whilst radicals are encapsulated in ellipses. Dashed boxes indicate group of isomers of the same species. With relative abundance to H2 provided beneath their molecular formula, species that have been detected in this study are in red boldface. Upper limits are also provided for undetected species. The rest of species that don’t have relative abundance or upper limit provided are those that have not been identified either due to lack of spectroscopic data or due to lack of sensitivity in observations and are also indicated in G+0.693. Figure 5. View largeDownload slide Chemical diagram of N-bearing species to illustrate the relationship between each individual species. Complexity increases from right to left by adding C (four areas, C, C–C, C–C–C, and n–C divided by vertical line), from top to bottom by adding H, and addition of N is indicated in diagonal direction. Molecules are encapsulated in square boxes whilst radicals are encapsulated in ellipses. Dashed boxes indicate group of isomers of the same species. With relative abundance to H2 provided beneath their molecular formula, species that have been detected in this study are in red boldface. Upper limits are also provided for undetected species. The rest of species that don’t have relative abundance or upper limit provided are those that have not been identified either due to lack of spectroscopic data or due to lack of sensitivity in observations and are also indicated in G+0.693. Figure 6. View largeDownload slide Chemical diagram of O-containing N-bearing species that are potentially related. Same notations as used in Fig. 5 except for O-addition instead of N-addition in diagonal direction. Figure 6. View largeDownload slide Chemical diagram of O-containing N-bearing species that are potentially related. Same notations as used in Fig. 5 except for O-addition instead of N-addition in diagonal direction. Table 7. Observational molecular abundances between related molecules. Molecules Abundance ratios Isomers G+0.693 SgrB2(N) Orion KL IRAS16293 TMC-1 HCCCN:HCCNC:HNCCC 1:0.03:0.003 – – 1:≤0.8:≤0.1 1:0.04:0.002 HNCO:HOCN:HCNO:HONC 1:0.006:≤0.001:≤0.0003 1:0.005 – 1:≤0.0002:≤0.0007a 1:≤0.0008 CH3NCO:CH3OCN:CH3CNO 1:≤0.1:≤0.02 1: – : ≤0.01 – 1:≤0.1:≤0.01a – Parents and daughters species HC3N:HC5N : HC7N : HC9N 1:0.3:0.02:≤0.005 1:0.004: – : – 1:0.1: – : – 1:0.09:≤1 1:0.25:0.19:0.04 CH3CN:C2H3CN:C2H5CN:n-C3H7CN 1:0.8:0.4:≤0.01 1:0.4:0.9:0.008 1 : 0.5 : 1.1 1:≤8:1.2:≤0.05 1:1.6 CH3CN:CH3C3N:CH3C5N 1:0.4:≤0.01 – – 1:≤0.002:≤0.01 1:0.2 CH3NH2:CH2NH 1:0.1 1:1.3 – – – HNCO:NH2CHO:CH3NCO 1:0.2:0.02 1:1:0.03 1:0.02:0.07 1:1:0.08 – Molecules Abundance ratios Isomers G+0.693 SgrB2(N) Orion KL IRAS16293 TMC-1 HCCCN:HCCNC:HNCCC 1:0.03:0.003 – – 1:≤0.8:≤0.1 1:0.04:0.002 HNCO:HOCN:HCNO:HONC 1:0.006:≤0.001:≤0.0003 1:0.005 – 1:≤0.0002:≤0.0007a 1:≤0.0008 CH3NCO:CH3OCN:CH3CNO 1:≤0.1:≤0.02 1: – : ≤0.01 – 1:≤0.1:≤0.01a – Parents and daughters species HC3N:HC5N : HC7N : HC9N 1:0.3:0.02:≤0.005 1:0.004: – : – 1:0.1: – : – 1:0.09:≤1 1:0.25:0.19:0.04 CH3CN:C2H3CN:C2H5CN:n-C3H7CN 1:0.8:0.4:≤0.01 1:0.4:0.9:0.008 1 : 0.5 : 1.1 1:≤8:1.2:≤0.05 1:1.6 CH3CN:CH3C3N:CH3C5N 1:0.4:≤0.01 – – 1:≤0.002:≤0.01 1:0.2 CH3NH2:CH2NH 1:0.1 1:1.3 – – – HNCO:NH2CHO:CH3NCO 1:0.2:0.02 1:1:0.03 1:0.02:0.07 1:1:0.08 – Note. a Upper limit estimated from chemical model in Quénard et al. (2017b). was possibly View Large Table 7. Observational molecular abundances between related molecules. Molecules Abundance ratios Isomers G+0.693 SgrB2(N) Orion KL IRAS16293 TMC-1 HCCCN:HCCNC:HNCCC 1:0.03:0.003 – – 1:≤0.8:≤0.1 1:0.04:0.002 HNCO:HOCN:HCNO:HONC 1:0.006:≤0.001:≤0.0003 1:0.005 – 1:≤0.0002:≤0.0007a 1:≤0.0008 CH3NCO:CH3OCN:CH3CNO 1:≤0.1:≤0.02 1: – : ≤0.01 – 1:≤0.1:≤0.01a – Parents and daughters species HC3N:HC5N : HC7N : HC9N 1:0.3:0.02:≤0.005 1:0.004: – : – 1:0.1: – : – 1:0.09:≤1 1:0.25:0.19:0.04 CH3CN:C2H3CN:C2H5CN:n-C3H7CN 1:0.8:0.4:≤0.01 1:0.4:0.9:0.008 1 : 0.5 : 1.1 1:≤8:1.2:≤0.05 1:1.6 CH3CN:CH3C3N:CH3C5N 1:0.4:≤0.01 – – 1:≤0.002:≤0.01 1:0.2 CH3NH2:CH2NH 1:0.1 1:1.3 – – – HNCO:NH2CHO:CH3NCO 1:0.2:0.02 1:1:0.03 1:0.02:0.07 1:1:0.08 – Molecules Abundance ratios Isomers G+0.693 SgrB2(N) Orion KL IRAS16293 TMC-1 HCCCN:HCCNC:HNCCC 1:0.03:0.003 – – 1:≤0.8:≤0.1 1:0.04:0.002 HNCO:HOCN:HCNO:HONC 1:0.006:≤0.001:≤0.0003 1:0.005 – 1:≤0.0002:≤0.0007a 1:≤0.0008 CH3NCO:CH3OCN:CH3CNO 1:≤0.1:≤0.02 1: – : ≤0.01 – 1:≤0.1:≤0.01a – Parents and daughters species HC3N:HC5N : HC7N : HC9N 1:0.3:0.02:≤0.005 1:0.004: – : – 1:0.1: – : – 1:0.09:≤1 1:0.25:0.19:0.04 CH3CN:C2H3CN:C2H5CN:n-C3H7CN 1:0.8:0.4:≤0.01 1:0.4:0.9:0.008 1 : 0.5 : 1.1 1:≤8:1.2:≤0.05 1:1.6 CH3CN:CH3C3N:CH3C5N 1:0.4:≤0.01 – – 1:≤0.002:≤0.01 1:0.2 CH3NH2:CH2NH 1:0.1 1:1.3 – – – HNCO:NH2CHO:CH3NCO 1:0.2:0.02 1:1:0.03 1:0.02:0.07 1:1:0.08 – Note. a Upper limit estimated from chemical model in Quénard et al. (2017b). was possibly View Large 4.1.1 Cyanopolyynes HCnN group Linear carbon chain radical C3N and cyanopolyynes chains, HCnN (n = 3, 5, 7 etc.) ranging from HC3N through HC7N, have been detected towards G+0.693 in this study. Besides the metastable isomer HC3N, the isomers HCCNC and HNCCC have also been detected. The main isomer HC3N seems to be ubiquitous in the ISM as it has been detected in all sources with relatively high abundances (≥10−9; Fig. 4 and Table 7). The HC3N/H2 abundance obtained in G+0.693 is a factor of 5 lower than those found towards L1 157-B1 and TMC-1 whilst the HC5N/H2 abundance in G+0.693 is consistent with that measured in L1157-B1 (Mendoza et al. 2018). As shown in Table 7, the relative abundance for molecules following the sequence from HC3N to HC7N is reduced by approximately a factor of 10 with an increasing molecular size in G+0.693. Although the abundance ratio between HC3N:HCN5 (∼1:0.3) matches very well between G+0.693 and TMC-1, both of the HC3N:HC7N and HC3N:HC9N ratio differ by an order of magnitude, respectively, between these two regions. Recently, cyanopolyynes have also been detected in the intermediate mass protocluster OMC2-FIR4 by Fontani et al. (2017). HC3N:HC5N abundance ratio (between 1:0.08 and 1:0.25) has been measured towards the eastern region of FIR4 where strong HC5N emission is found. Through their analysis along with relevant gas-phase chemical model, it has been suggested that HC3N:HC5N ≥0.08 obtained in FIR4 was possibly due to an enhanced cosmic ray ionization rate of ζ = 4 × 10−14 s−1. Towards G+0.693, the HC3N:HC5N abundance ratio is 1:0.3, consistent with the range obtained in FIR4. This might suggest that cosmic rays can also affect the chemistry of cyanopolyynes in G+0.693 (note that the cosmic ray ionization rate in the CMZ is constrained to be ζ ∼ 1–10 × 10−15 s−1, which is factors 4–40 lower than that in FIR4; Yusef-Zadeh et al. 2013; Ginsburg et al. 2016). Alternatively, a low HC3N:HC5N abundance ratio could be the result of a cold temperature chemistry as proposed by Jaber et al. (2017). These authors inferred HC3N:HC5N abundance ratios of 1:∼10 and 1:∼1 toward the inner hot corino and the outer cold envelope of IRAS16293, respectively. The HC3N:HC5N ratio of the cold envelope is comparable to that of G+0.693. In their models, Jaber et al. (2017) propose that an enhanced cosmic ray ionization rate has little effect on the abundance of HC3N, although their predicted abundance of HC5N does not match the observations. Note also that while the cold envelope of IRAS16293 has a kinetic temperature of Tkin = 20 K, Tkin in G+0.693 is  73–140 K (see Section 3.2). This implies that the cold chemistry characteristic of the envelope of IRAS16293 cannot be applied to G+0.693. Being the simplest member of the cyanopolyynes, the most important formation pathway of HC3N has been proposed to be the neutral–neutral reaction (mechanism 1 hereafter) between hydrocarbon molecules CnH2 (n= 1, 2, 3, 4...) and the CN radical. For instance, the gas-phase reaction between C2H2 and CN to form HC3N (see equation 1 in Burkhardt et al. 2017) has been supported by several studies towards dark clouds, low-mass, and high-mass star-forming regions (Takano et al. 1998; Taniguchi, Saito & Ozeki 2016; Burkhardt et al. 2017; Taniguchi, Ozeki & Saito 2017). Subsequently, larger cyanopolyynes such as HC5N and HC7N would be assumed to be produced from CN to the same extent as HC3N. A recent detection of the isotopologues of HC5N and HC7N in TMC-1 by Burkhardt et al. (2017) has provided evidence that HC5N and HC7N do not necessarily undergo the same formation pathway as suggested for HC3N. Instead, the dominant formation mechanism for these two cyanopolyynes could be the reaction in gas phase of hydrocarbon ions and nitrogen atoms (mechanism 2 hereafter; see equation 3 in Burkhardt et al. 2017). Since the gas kinetic temperature in G+0.693 is high ( 73–140 K), we have investigated how the reaction rates of these two mechanisms vary with temperature to evaluate their efficiency.7 For mechanism 1, the reaction rates remain almost constant (they vary by less than a factor of 2) with increasing temperature from 10 to 100 K. This cannot explain a factor of 5 difference in the abundance of HC3N between G+0.693 and TMC-1. Hence we propose that gas-phase reactions involving CN might not be the main formation route for HC3N. For mechanism 2, however, the reaction rates slow down by a factor of ∼5 from 10 to 100 K, which would explain why HC5N and HC7N systematically show lower abundances in G+0.693 compared to TMC-1. All these point towards the idea that CN is not necessarily the ‘parent’ molecule of cyanopolynnes. Instead, an enhanced cosmic ray flux, as found in the Galactic Center, would increase the abundance of ionized hydrocarbons, explaining the large HC3N abundance and the cyanopolyynes ratios observed in G+0.693. For the less stable isomers HCCNC and HNCCC, their abundances are systematically lower by a factor of 4 than those measured in TMC-1. HNCCC, the least stable isomer amongst the three molecules, presents the lowest abundance as expected. However, we found that the HCCCN:HCCNC:HNCCC ratios are almost uniform between G+0.693 and TMC-1 (see Table 7). Recently, HCCNC and HNCCC have been detected in the prestellar core L1544 (Vastel et al. 2018). Together with previous detection of HC3N by Quénard et al. (2017a), the HCCCN:HCCNC:HNCCC abundance ratio towards L1544 is 1:0.04–0.14:0.003–0.01. This ratio is also marginally consistent with those obtained from G+0.693 and TMC-1. 4.1.2 Nitrile, –CN group The closely related cyanides CH3CN, C2H3CN, and C2H5CN have all been detected towards Sgr B2(N) and Orion KL, as well as G+0.693. Consistent molecular ratios are found between these three molecules towards Sgr B2N and Orion KL (i.e. 1:0.4:0.9 and 1:0.5:1.1, respectively), but a factor of 2 variation is present when compared to G+0.693 (1:0.8:0.4). Furthermore, a strong correlation has also been found for the abundances of C2H3CN and C2H5CN towards six hot molecular cores (HMCs), giving the abundance ratio C2H3CN:C2H5CN = 1:∼2–3.3 (Fontani et al. 2007). Similar ratios such as 1:2.25 and 1:2.2 are obtained towards Sgr B2N and Orion KL, respectively. This is expected since hot cores are thought to have higher abundances of saturated molecules than those of unsaturated ones. Note here that the term ‘saturated’ refers to carbon chain molecules involving one single C–C bond whereas the term ‘unsaturated’ implies carbon chain molecules containing carbon–carbon double bonds (C=C) or triple bonds (C≡C) (Herbst & Dishoeck 2009). And more importantly, C2H5CN in hot cores is thought to be formed on to icy mantles of dust grains via sequential hydrogenation of C3N and then evaporated to form C2H3CN through ion–molecule reactions in the gas phase (Caselli, Hasegawa & Herbst 1993). On the contrary, unsaturated C2H3CN appears to be slightly more abundant than saturated C2H5CN in G+0.693, yielding an opposite ratio of C2H3CN:C2H5CN as 1:0.5 compared to hot cores. If we compare our results to the modelling of Caselli et al. (1993) for the Orion compact ridge, the ion–molecule gas-phase chemistry can convert the saturated C2H5CN into unsaturated C2H3CN efficiently, thanks to two different reasons: (i) the high cosmic ray flux present in the GC that increases the fractional ionic abundance; and (ii) the lower densities of ∼104 cm−3 of G+0.693, which are also expected to yield higher ion densities in the gas (see section 3 in Caselli et al. 1993). In this way, for time-scales of about 105 yr, C2H3CN can become more abundant than C2H5CN, which is consistent with our results. In addition, the CH3CN/H2 abundance ratio measured in G+0.693 matches very well with that of the shocked-region L1157-B1. 4.1.3 Amine, –NH/–NH2 group Among the studies we considered for our comparison, amino group species have mostly been detected in large abundance towards the high-mass star-forming region Sgr B2(N). Recently, NH2CN has been reported by Coutens et al. (2017a) towards IRAS16293 and NGC1333 IRAS2A hot corinos. Therefore, our knowledge of these amine group species has been restricted to regions with high temperatures and active star formation. In this study, four different amine group species have been detected towards G+0.693, for which three out of four species present similar molecular abundances to those measured towards Sgr B2(N). The large abundance difference found for methanimine (CH2NH; see Fig. 4) can possibly be explained by the increased hydrogenation efficiency in G+0.693, which is expected to yield a higher abundance of methylamine (CH3NH2). One of the possible formation routes of CH3NH2 is via hydrogenation of HCN on grain surfaces (Theule et al. 2011). Grain surface hydrogenation of CH2NH can also lead to the formation of CH3NH2 at low temperatures (T = 15 K). Hence, CH2NH is suggested to be a hydrogenation-intermediate species between HCN and CH3NH2 (Theule et al. 2011). The abundance ratio of CH3NH2:CH2NH in G+0.693 is 1:0.1, whereas in the hot cores G10.47+00.3 and Sgr B2N, opposite ratios of 1:6 and 1:1.3 are obtained, respectively (Belloche et al. 2013; Ohishi et al. 2017). Since CH3NH2 is likely formed via hydrogenation from HCN, the opposite trend for the CH3NH2:CH2NH ratio could be caused by a more efficient hydrogenation on dust grains in G+0.693 than in hot cores. In addition, coupled to the low-dust temperatures measured in G+0.693, the chemistry in grain mantles is also likely to be affected by energetic processing in the GC such as X-rays and/or cosmic rays, which increases the availability of atomic H in the gas phase and, subsequently, in the grain mantles. Indeed, Armijos-Abendaño et al. (2014) (source named as LOS+0.693 in the paper) have concluded that a chemistry driven by X-rays could be expected due to the presence of strong Fe Kα line emission towards G+0.693, while UV photochemistry in G+0.693 is rather uncertain. Consequently, a larger degree of hydrogenation is possible in G+0.693 with respect to hot cores. Similarly, Requena-Torres et al. (2008) have shown that the abundance ratio of the O-bearing pair H2CCO:CH3CHO in G+0.693 (1:3.6) is completely different from that observed in hot cores (1:0.2). A more efficient hydrogenation in the CMZ with respect to hot cores was suggested in their study, which is consistent with our CH3NH2:CH2NH. For the rest of amine species, similar abundances are derived in G+0.693 and Sgr B2(N) which suggests that they may arise from the same environment, presumably the envelope of the Sgr B2 cloud. For other species within the same group, NH2CN:NH2CHO abundance ratio in IRAS16293 (1:5) is found to be an order of magnitude lower than that in IRAS2A (1:50) where the latter falls within the range inferred towards Sgr B2(N) (1:25–50). For G+0.693, this ratio is found to be 1:2, which is consistent with the ratio measured in Orion KL (1:∼0.7–2.5) and about a factor of 2.5 lower than in IRAS16293. We speculate that the hydrogenation of NH2 and CN radicals may be more efficient than the formation of NH2CN since the enhanced cosmic ray flux is likely to produce larger amounts of atomic hydrogen, which are then available in the gas phase to accrete on to the surface of dust grains. 4.1.4 Cyanate, –NCO group Cyanate group molecules have attracted much attention in recent years due to their possible chemical link to the building blocks of life. N–C=O is often known as a peptide bond, which represents the linkage between two amino acids in protein chains. Several of these molecules with peptide-like bonds are detected in G+0.693 such as isocyanic acid (HNCO), formamide (NH2CHO), and methyl isocyanate (CH3NCO). In the family of HNCO isomers, only the two most stable isomers HNCO and HOCN have been detected towards G+0.693 (HOCN was first observed in this source, named as SgrB2M offset 20’, 100’ in the paper by Brünken et al. 2010) and upper limits are provided for the other two isomers HCNO and HONC (see Table3). HNCO in G+0.693 is almost as abundant as in L1157−B1 and only about a factor of 4 less abundant than in Sgr B2N. Martín et al. (2008) have proposed that shocks may explain the high HNCO abundances measured in Galactic nuclei. Observations carried out towards the L1157 molecular outflow (Rodríguez-Fernández et al. 2010) also support the idea that the enhancement of HNCO in star-forming regions is due to shocks. Furthermore, two independent studies, Kelly et al. (2017) and Yu, Xu & Wang (2017), have concluded that an enhancement of HNCO abundance may indicate the presence of a slow shock (∼20 km−1). On the whole, the consistent abundances of HNCO detected in these three regions imply that their physical environments are likely affected by low-/moderate-velocity shocks. The fairly constant abundance ratio of HNCO:HOCN (∼1:0.005) between G+0.693 and Sgr B2N obtained in this study agrees very well with the HNCO:HOCN ratios derived from several chemically and physically distinct regions in Sgr B2 (see Table 3 in Brünken et al. 2010). This further supports the hypothesis that, like HNCO, sputtering of grain mantles by shock waves is responsible for the observed abundance of HOCN in these regions. The HNCO:HOCN:HCNO ratio behaves differently in G+0.693, and gives the ratio 1:0.006:≤0.001 compared to that in the proto-star IRAS16293 (1:≤0.0002:≤0.0007) and in the prestellar core L1544 (1:0.03:0.02, methanol peak; see Quénard et al. 2017b). This might be potentially related to the destruction rates of HOCN and HCNO via the gas-phase reactions HOCN/HCNO + O, since both are sensitive to the gas temperature (Quénard et al. 2017b). Their reaction rates increase rapidly from 10−18 to 10−10 cm3 s−1 for temperatures from 10 to 300 K, which would yield more extreme HNCO:HOCN:HCNO ratios in IRAS16293. In this context, with a gas temperature of ∼ 100–150 K, the destruction rates of HOCN and HCNO in G+0.693 are not as efficient as those in IRAS16293, but certainly faster than in L1544. Consequently, the HNCO:HOCN:HCNO ratio in G+0.693 is expected to lie within the corresponding ratios obtained from these two regions, as observed. Besides the family of HNCO isomers, NH2CHO, and HNCO have been proposed to be chemically related (Mendoza et al. 2014; López-Sepulcre et al. 2015). However, recent modelling by Quénard et al. (2017b) has shown that these two species, rather than being chemically linked, respond in the same manner to environmental conditions, precisely to the temperature. This is consistent with the measured HNCO:NH2CHO abundance ratio in G+0.693 (1:0.2), which lies between those derived towards the prestellar core L1544 (methanol peak, 1:≤0.03) and the hot corino IRAS16293 (1:1). Note that the gas temperature progressively increases from L1544 (∼10 K) to G+0.693 (≤145 K), and to the IRAS16293 hot corino (∼100 – 300 K). The measured abundance of NH2CHO in G+0.693 is much higher than those observed in both IRAS16293 (≤1.9 × 10−9; Martín-Doménech et al. 2017) and L1544 (≤(6.7 − 8.7) × 10−13; Jiménez-Serra et al. 2016), but very similar to that inferred from the L1157 molecular outflow (Mendoza et al. 2014). It is possible either that NH2CHO is formed in shocks via gas–phase reactions (Codella et al. 2017) or that it is formed on grain surfaces and then ejected from dust grains in shocks (Quénard et al. 2017b). In Table 7, we present the abundance ratio between HNCO, NH2CHO, and other related molecules such as NCO and CH3NCO. The latter molecule CH3NCO is considered as the relevant precursor in the formation of prebiotic species that has recently been detected in several sources such as Sgr B2N (Halfen et al. 2015; Belloche et al. 2017), Orion KL (Cernicharo et al. 2016), IRAS16293 (Ligterink et al. 2017; Martín-Doménech et al. 2017), and even in comet 67P/Churyumov–Gerasimenko (Goesmann et al. 2015). We find that the HNCO:CH3NCO ratio in G+0.693 is a factor of 4, 5.5, and 3.5 lower than those measured in IRAS16293, Sgr B2N, and Orion KL, respectively. In contrast, the NH2CHO:CH3NCO ratio is a factor of 1.3 and 3.3 higher than those measured in IRAS16293 and Sgr B2N, respectively, but a factor of 35 lower than that in Orion KL. The measured discrepancies in the abundance ratio between these regions point to a different chemical evolution of the grain mantles in the GC and in the disc (see Requena-Torres et al. 2008). 4.2 Comparison with extragalactic environments The QGMC G+0.693 in the GC is not only one of the most promising sources to constrain the formation pathway of COMs detected in the ISM, but also an excellent candidate to enhance our knowledge of the chemical complexity expected in the nuclei of external galaxies and influence the different extragalactic sources. There are an increasing number of N-bearing species being detected across different extragalactic environments, evidencing similarities as well as differences compared to galactic environments. For example, the first detection of NO in the starburst galaxy NGC 253 by Martín et al. (2003, 2006) presents an abundance consistent with that measured in G+0.693, Sgr B2N, and Orion KL. The rare molecule NH2CN has also been detected towards NGC 253 with an abundance of ∼2 × 10−10 (Martín et al. 2006; Aladro et al. 2015). This abundance is an order of magnitude lower than that in Sgr B2N and G+0.693 but matches up with the latest measurement towards IRAS16293 (Coutens et al. 2017b). HCN, HNC, CH3CN, HNCO, and HC3N have also been detected in several extragalactic sources (Martín et al. 2006; Aladro et al. 2011, 2015; Costagliola et al. 2015; Harada et al. 2018). The most accepted idea is that the chemistry of the most complex molecules in starburst galaxies is dominated by the ejection of the icy grain mantel by low-velocity shock followed by different types of gas phase chemistry depending on the dominant environment (UV radiation, X-rays, cosmic rays, high-gas temperature etc.). One of the most interesting results from our study of G+0.693 is that one can use the HC3N/HC5N abundance ratio as a tracer of enhanced cosmic ray fluxes. So far, larger cyanopolyynes such as HC5N, however, have only been detected in two extragalactic sources: NGC 253 (Aladro et al. 2015) and the luminous infrared galaxy NGC 4418 (Costagliola et al. 2015). The derived HC3N:HC5N column density ratios of NGC253 and NGC 4418 are 1:0.7 and 1:0.6 respectively, i.e. they are at least higher by factors of 3 than those measured in any other galactic source (see Table 7). These large ratios would be consistent with the scenario of enhanced cosmic rays. This would indicate that the nuclei of both active galaxies would have a cosmic ray flux even larger than that inferred for the GC. For both sources similar conclusion has been reached using other tracers. Using the emission from OH+, H2O+, and H3O+, González-Alfonso et al. (2012) concluded that X-rays/cosmic ray ionization from the AGN is very likely responsible for the large abundance of these molecules. Bradford et al. (2003) have analysed the emission of high J-line of CO and concluded that the best mechanism for heating the gas is cosmic rays with a flux 800 times that of the Galaxy. 4.3 The origin of N-bearing species in G+0.693 The QGMC G+0.693 is located towards the north-east of the Sgr B2 star-forming complex, but it does not show any signposts of recent or ongoing star formation in the form of Ultra Compact H ii regions or H2O masers (Güsten, Walmsley & Pauls 1981; Hüettemeister et al. 1993; Martín-Pintado et al. 1997; Ginsburg et al. 2018). Same as other QGMCs within the CMZ, its physical properties are characterized by high-gas kinetic temperatures and cold-dust temperatures (Guesten et al. 1985; Rodríguez-Fernández et al. 2001, 2004). As discussed throughout Section 4.1, the chemistry of this QGMC is rather unique since it shows high abundances not only of O-bearing species (Requena-Torres et al. 2008), but also of N-bearing molecules. The reason why this source presents such a rich chemistry in COMs among all QGMCs in the Galactic Center remains however unclear. The comparison of the abundances of N-bearing COMs measured in G+0.693 with those derived towards other Galactic environments (as discussed in section 4.1) provides clues of the main mechanism(s) responsible for the rich COM chemistry found in this source: Large carbon chain cyanopolyynes (from HC3N to HC7N) are clearly detected towards G+0.693. Systematically lower abundances of HC3N, HC5N, and HC7N are found in G+0.693 compared to the molecular dark cloud TMC-1. This indicates that CN might not necessarily be the ‘parent’ molecule of cyanopolyynes. In addition, the lower HC5N:HC7N ratios observed in G+0.693 may be due to the presence of enhanced cosmic rays ionization rates in the Galactic Center. In G+0.693, the saturated molecule CH3NH2 appears to be more abundant than the unsaturated radical CH2NH whilst the opposite is true in Galactic hot cores. A more efficient hydrogenation mechanism on dust grains is proposed for G+0.693 than for hot cores in the Galactic disc. The most likely scenario to explain the large abundance of unsaturated C2H3CN compared with that of the saturated C2H5CN is ion–molecule gas phase chemistry fostered by an enhanced cosmic ray flux. Comparison of the abundance ratio of peptide-like (–CNO group) species across multiple environments shows large differences, which supports the previous claim in Requena-Torres et al. (2008) of a different grain mantle composition due to a different chemical evolution of the grain mantles in the GC with respect to the Galactic disc. Remarkable consistences between the molecular abundance measured in G+0.693 and in the shocked region L1157−B1 (e.g. CH3CN, HC5N, HNCO, and NH2CHO) are found. This emphasizes the idea that a large fraction of the ices from dust grains has been injected into the gas phase via grain sputtering in widespread low-velocity shock waves. It has been proposed that G+0.693 is located between two streams of molecular gas that seem to be merging (Hasegawa et al. 1994; Henshaw et al. 2016). This may yield a cloud–cloud collision that drives large-scale, low-velocity shocks in the region, and which ultimately sputters dust grains icy mantles efficiently. This QGMC indeed shows the highest abundances of HNCO in the sample of Martín et al. (2008, source SgrB2M offset 20’, 100’ in that paper), supporting the idea that this source is mainly dominated by low-velocity shock. This would also explain the similarity in the abundances of N-bearing species measured towards G+0.693 and L1157-B1 (see Section 4.1). Some of the derived abundance ratios (such as e.g. HC5N:HC7N, and the high abundance of C2H3CN) indicate that energetic processing by e.g. X-rays and/or cosmic rays may strongly affect the gas phase chemistry of this cloud owing to its location in the CMZ. 5 CONCLUSIONS Using the GBT and IRAM 30 m telescopes, we performed an unbiased spectral line survey towards the GC QGMC G+0.693. The survey covers partially the 1 cm and 1 mm spectral window and fully the 2 mm, and 3 mm atmospheric windows. We explore the chemical richness in terms of presence and abundance of N-bearing species in the GC QGMC G+0.693. In this study, we have reported 17 clear detections and two tentative detections of N-bearing species. These species show very high abundances relative to H2, ranging from 10−11 to 10−8. The comparison across various Galactic environments allows us to constrain possible mechanisms responsible for the unique chemistry observed in G+0.693; grain sputtering by widespread low-velocity shocks is by far the most promising mechanism to activate the chemistry in this source. However, energetic processing by either X-rays and/or cosmic rays needs to be invoked in order to explain some of the observed abundance ratios between molecules within the same family. Partial contribution from gas-phase chemistry cannot be ruled out either for some particular cases. The comparison of the measured molecular abundance in G+0.693 with those derived in extragalactic sources shows that G+0.693 is an excellent template where to elucidate the chemical complexity expected in future extragalactic surveys done with ALMA. Although the current data have highlighted an extremely rich organic inventory in G+0.69 with abundant amounts of complex N-bearing species, the nature of this source has not yet been unveiled in detail. Interferometric maps are urged to establish the morphology and small-scale physical structure and thus understand fundamentally the chemical stratification in this source. New observations with greater sensitivity and higher angular resolution, as well as more laboratory experiments and theoretical models, are crucial to investigate further the origin of COMs not only in G+0.693 but also in extragalactic sources. SUPPORTING INFORMATION Supplementary data are available at MNRAS online. Please note: Oxford University Press is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. ACKNOWLEDGEMENTS We thank the anonymous referee for his/her instructive comments and suggestions. SZ acknowledges support through a Principal’s studentship funded by Queen Mary University of London. IJ-S acknowledges the financial support received from the STFC through an Ernest Rutherford Fellowship (proposal number ST/L004801). VMR has received funding from the European Union’s H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 664931. JM-P has been partially supported by the Spanish MINECO under grant numbers: ESP2015-65597-C4-1-R and ESP2017-86582-C4-1-R. DR acknowledges support from the Collaborative Research Council 956, subproject A5, funded by the Deutsche Forschungsgemeinschaft (DFG). Footnotes 1 https://www.astro.uni-koeln.de/cdms/molecules 2 The National Radio Astronomy Observatories is a facility of the National Science Foundation, operated under a cooperative agreement by Associated Universities, Inc. 3 http://www.iram.fr/IRAMFR/GILDAS. 4 Madrid Data Cube Analysis on ImageJ is a software developed in the Centre of Astrobiology (Madrid, INTA-CSIC) to visualize and analyse single spectra and data cubes (Rivilla et al. 2016; Martín et al., in preparation.) 5 http://spec.jpl.nasa.gov/ 6 http://www.astro.uni-koeln.de/cdms 7 Reaction rates extracted from the KIDA data base (http://kida.obs.ubordeaux1.fr/). REFERENCES Aladro R. , Martín-Pintado J. , Martín S. , Mauersberger R. , Bayet E. , 2011 , A&A , 525 , A89 https://doi.org/10.1051/0004-6361/201014090 CrossRef Search ADS Aladro R. et al. , 2015 , A&A , 579 , A101 https://doi.org/10.1051/0004-6361/201424918 CrossRef Search ADS Arce H. G. , Santiago-García J. , Jørgensen J. K. , Tafalla M. , Bachiller R. , 2008 , Astrophys. J. Lett. , 681 , L21 https://doi.org/10.1086/590110 CrossRef Search ADS Armijos-Abendaño J. , Martín-Pintado J. , Requena-Torres M. A. , Martín S. , Rodríguez-Franco A. , 2014 , MNRAS , 446 , 3842 https://doi.org/10.1093/mnras/stu2271 CrossRef Search ADS Bacmann A. , Taquet V. , Faure A. , Kahane C. , Ceccarelli C. , 2012 , A&A , 541 , L12 https://doi.org/10.1051/0004-6361/201219207 CrossRef Search ADS Bally J. , Stark A. A. , Wilson R. W. , Henkel C. , 1987 , ApJS , 65 , 13 https://doi.org/10.1086/191217 CrossRef Search ADS Balucani N. , Ceccarelli C. , Taquet V. , 2015 , MNRAS , 449 , L16 https://doi.org/10.1093/mnrasl/slv009 CrossRef Search ADS Belloche A. , Garrod R. T. , Müller H. S. P. , Menten K. M. , Comito C. , Schilke P. , 2009 , A&A , 499 , 215 https://doi.org/10.1051/0004-6361/200811550 CrossRef Search ADS Belloche A. , Müller H. S. P. , Menten K. M. , Schilke P. , Comito C. , 2013 , A&A , 559 , A47 https://doi.org/10.1051/0004-6361/201321096 CrossRef Search ADS Belloche A. , Müller H. S. P. , Garrod R. T. , Menten K. M. , 2016 , A&A , 587 , A91 https://doi.org/10.1051/0004-6361/201527268 CrossRef Search ADS Belloche A. et al. , 2017 , A&A , 601 , A49 https://doi.org/10.1051/0004-6361/201629724 CrossRef Search ADS Bisschop S. E. , Jørgensen J. K. , Bourke T. L. , Bottinelli S. , van Dishoeck E. F. , 2008 , A&A , 488 , 959 https://doi.org/10.1051/0004-6361:200809673 CrossRef Search ADS Blake G. A. , Sutton E. C. , Masson C. R. , Phillips T. G. , 1987 , ApJ , 315 , 621 https://doi.org/10.1086/165165 CrossRef Search ADS Boucher D. , Burie J. , Demaison J. , Dubrulle A. , Legrand J. , Segard B. , 1977 , J. Mol. Spectrosc. , 64 , 290 https://doi.org/10.1016/0022-2852(77)90267-3 CrossRef Search ADS Bradford C. M. , Nikola T. , Stacey G. J. , Bolatto A. D. , Jackson J. M. , Savage M. L. , Davidson J. A. , Higdon S. J. , 2003 , ApJ , 586 , 891 https://doi.org/10.1086/367854 CrossRef Search ADS Brünken S. , Belloche A. , Martín S. , Verheyen L. , Menten K. M. , 2010 , A&A , 516 , A109 https://doi.org/10.1051/0004-6361/200912456 CrossRef Search ADS Burkhardt A. M. , Herbst E. , Kalenskii S. V. , McCarthy M. C. , Remijan A. J. , McGuire B. A. , 2017 , MNRAS , 474 , 5068 CrossRef Search ADS Calcutt H. , Viti S. , Codella C. , Beltrán M. T. , Fontani F. , Woods P. M. , 2014 , MNRAS , 443 , 3157 https://doi.org/10.1093/mnras/stu1363 CrossRef Search ADS Caselli P. , Hasegawa T. I. , Herbst E. , 1993 , ApJ , 408 , 548 https://doi.org/10.1086/172612 CrossRef Search ADS Caux E. et al. , 2011 , A&A , 532 , A23 https://doi.org/10.1051/0004-6361/201015399 CrossRef Search ADS Cazzoli G. , Puzzarini C. , 2006 , J. Mol. Spectrosc. , 240 , 153 https://doi.org/10.1016/j.jms.2006.09.013 CrossRef Search ADS Cernicharo J. et al. , 2016 , A&A , 587 , L4 https://doi.org/10.1051/0004-6361/201527531 CrossRef Search ADS Codella C. et al. , 2009 , A&A , 507 , L25 https://doi.org/10.1051/0004-6361/200913340 CrossRef Search ADS Codella C. et al. , 2017 , A&A , 605 , L3 https://doi.org/10.1051/0004-6361/201731249 CrossRef Search ADS Cordiner M. A. , Charnley S. B. , Kisiel Z. , McGuire B. A. , Kuan Y.-J. , 2017 , ApJ , 850 , 187 https://doi.org/10.3847/1538-4357/aa970c CrossRef Search ADS Costagliola F. et al. , 2015 , A&A , 582 , A91 https://doi.org/10.1051/0004-6361/201526256 CrossRef Search ADS Coutens A. , Viti S. , Rawlings J. M. C. , Beltrán M. T. , Holdship J. , Jiménez-Serra I. , Quénard D. , Rivilla V. M. , 2017a , MNRAS , 475 , 2016 CrossRef Search ADS Coutens A. et al. , 2017b , A&A , 612 , A107 CrossRef Search ADS Cuadrado S. , Goicoechea J. R. , Pilleri P. , Cernicharo J. , Fuente A. , Joblin C. , 2015 , A&A , 575 , A82 https://doi.org/10.1051/0004-6361/201424568 CrossRef Search ADS Cuadrado S. , Goicoechea J. R. , Cernicharo J. , Fuente A. , Pety J. , Tercero B. , 2017 , A&A , 603 , A124 CrossRef Search ADS Dore L. , Bizzocchi L. , Degli Esposti C. , Gauss J. , 2010 , J. Mol. Spectrosc. , 263 , 44 https://doi.org/10.1016/j.jms.2010.06.012 CrossRef Search ADS Dore L. , Bizzocchi L. , Degli Esposti C. , 2012 , A&A , 544 , A19 https://doi.org/10.1051/0004-6361/201219674 CrossRef Search ADS Endres C. P. , Schlemmer S. , Schilke P. , Stutzki J. , Mller H. S. P. , 2016 , J. Mol. Spectrosc. , 327 , 95 https://doi.org/10.1016/j.jms.2016.03.005 CrossRef Search ADS Flower D. R. , Watt G. D. , 1984 , MNRAS , 209 , 25 https://doi.org/10.1093/mnras/209.1.25 CrossRef Search ADS Fontani F. , Pascucci I. , Caselli P. , Wyrowski F. , Cesaroni R. , Walmsley C. M. , 2007 , A&A , 470 , 639 https://doi.org/10.1051/0004-6361:20077485 CrossRef Search ADS Fontani F. et al. , 2017 , A&A , 605 , A57 https://doi.org/10.1051/0004-6361/201730527 CrossRef Search ADS Garrod R. T. , Herbst E. , 2006 , A&A , 457 , 927 https://doi.org/10.1051/0004-6361:20065560 CrossRef Search ADS Garrod R. T. , Widicus Weaver S. L. , Herbst E. , 2008 , ApJ , 682 , 283 https://doi.org/10.1086/588035 CrossRef Search ADS Gerin M. , Viala Y. , Casoli F. , 1993 , A&A , 268 , 212 Ginsburg A. et al. , 2016 , A&A , 586 , A50 https://doi.org/10.1051/0004-6361/201526100 CrossRef Search ADS Ginsburg A. G. et al. , 2018 , ApJ , 853 , 171 CrossRef Search ADS Goesmann F. et al. , 2015 , Science , 349 : https://doi.org/10.1126/science.aab0689 González-Alfonso E. et al. , 2012 , A&A , 541 , A4 https://doi.org/10.1051/0004-6361/201118029 CrossRef Search ADS Gratier P. , Pety J. , Guzmán V. , Gerin M. , Goicoechea J. R. , Roueff E. , Faure A. , 2013 , A&A , 557 , A101 https://doi.org/10.1051/0004-6361/201321031 CrossRef Search ADS Gratier P. , Majumdar L. , Ohishi M. , Roueff E. , Loison J. C. , Hickson K. M. , Wakelam V. , 2016 , ApJS , 225 , 25 https://doi.org/10.3847/0067-0049/225/2/25 CrossRef Search ADS Guesten R. , Walmsley C. M. , Ungerechts H. , Churchwell E. , 1985 , A&A , 142 , 381 Güsten R. , Walmsley C. M. , Pauls T. , 1981 , A&A , 103 , 197 Halfen D. T. , Ilyushin V. , Ziurys L. M. , 2011 , ApJ , 743 , 60 https://doi.org/10.1088/0004-637X/743/1/60 CrossRef Search ADS Halfen D. T. , Ilyushin V. V. , Ziurys L. M. , 2015 , ApJ , 812 , L5 https://doi.org/10.1088/2041-8205/812/1/L5 CrossRef Search ADS Harada N. , Sakamoto K. , Martin S. , Aalto S. , Aladro R. , Sliwa K. , 2018 , ApJ , 855 , 49 CrossRef Search ADS Hasegawa T. , Sato F. , Whiteoak J. B. , Miyawaki R. , 1994 , ApJ , 429 , L77 https://doi.org/10.1086/187417 CrossRef Search ADS Henshaw J. D. et al. , 2016 , MNRAS , 457 , 2675 https://doi.org/10.1093/mnras/stw121 CrossRef Search ADS Herbst E. , Dishoeck E. F. , 2009 , ARA&A , 47 , 427 https://doi.org/10.1146/annurev-astro-082708-101654 CrossRef Search ADS Hollis J. M. , Lovas F. J. , Jewell P. R. , 2000 , ApJ , 540 , L107 https://doi.org/10.1086/312881 CrossRef Search ADS Hollis J. M. , Lovas F. J. , Jewell P. R. , Coudert L. H. , 2002 , ApJ , 571 , L59 https://doi.org/10.1086/341148 CrossRef Search ADS Hollis J. M. , Lovas F. J. , Remijan A. J. , Jewell P. R. , Ilyushin V. V. , Kleiner I. , 2006 , ApJ , 643 , L25 https://doi.org/10.1086/505110 CrossRef Search ADS Hüettemeister S. , Wilson T. L. , Bania T. M. , Martín-Pintado J. , 1993 , A&A , 280 , 255 Jaber A. A. , Ceccarelli C. , Kahane C. , Caux E. , 2014 , ApJ , 791 , 29 https://doi.org/10.1088/0004-637X/791/1/29 CrossRef Search ADS Jaber A. A. et al. , 2017 , A&A , 597 , A40 https://doi.org/10.1051/0004-6361/201629506 CrossRef Search ADS Jiménez-Serra I. et al. , 2016 , ApJ , 830 , L6 https://doi.org/10.3847/2041-8205/830/1/L6 CrossRef Search ADS Jorgensen J. K. , Favre C. , Bisschop S. E. , Bourke T. L. , van Dishoeck E. F. , Schmalzl M. , 2012 , ApJ , 757 , L4 https://doi.org/10.1088/2041-8205/757/1/L4 CrossRef Search ADS Jørgensen J. K. et al. , 2016 , A&A , 595 , A117 https://doi.org/10.1051/0004-6361/201628648 CrossRef Search ADS Kahane C. , Ceccarelli C. , Faure A. , Caux E. , 2013 , ApJ , 763 , L38 https://doi.org/10.1088/2041-8205/763/2/L38 CrossRef Search ADS Kelly G. , Viti S. , García-Burillo S. , Fuente A. , Usero A. , Krips M. , Neri R. , 2017 , A&A , 597 , A11 https://doi.org/10.1051/0004-6361/201628946 CrossRef Search ADS Kirchhoff W. H. , Johnson D. R. , Lovas F. J. , 1973 , J. Phys. Chem. Ref. Data , 2 , 1 https://doi.org/10.1063/1.3253110 CrossRef Search ADS Krieger N. et al. , 2017 , ApJ , 850 , 77 https://doi.org/10.3847/1538-4357/aa951c CrossRef Search ADS Lefloch B. et al. , 2012 , ApJ , 757 , L25 https://doi.org/10.1088/2041-8205/757/2/L25 CrossRef Search ADS Lefloch B. , Ceccarelli C. , Codella C. , Favre C. , Podio L. , Vastel C. , Viti S. , Bachiller R. , 2017 , MNRAS , 469 , L73 https://doi.org/10.1093/mnrasl/slx050 CrossRef Search ADS Ligterink N. F. W. et al. , 2017 , MNRAS , 469 , 2219 https://doi.org/10.1093/mnras/stx890 CrossRef Search ADS López-Sepulcre A. et al. , 2015 , MNRAS , 449 , 2438 https://doi.org/10.1093/mnras/stv377 CrossRef Search ADS Lykke J. M. et al. , 2017 , A&A , 597 , A53 https://doi.org/10.1051/0004-6361/201629180 CrossRef Search ADS Marcelino N. , Cernicharo J. , Tercero B. , Roueff E. , 2009 , ApJ , 690 , L27 https://doi.org/10.1088/0004-637X/690/1/L27 CrossRef Search ADS Martín S. , Mauersberger R. , Martín-Pintado J. , García-Burillo S. , Henkel C. , 2003 , A&A , 411 , L465 https://doi.org/10.1051/0004-6361:20031442 CrossRef Search ADS Martín S. , Mauersberger R. , Martín-Pintado J. , Henkel C. , García-Burillo S. , 2006 , ApJS , 164 , 450 https://doi.org/10.1086/503297 CrossRef Search ADS Martín S. , Requena-Torres M. A. , Martín-Pintado J. , Mauersberger R. , 2008 , ApJ , 678 , 245 https://doi.org/10.1086/533409 CrossRef Search ADS Martín S. et al. , 2011 , A&A , 527 , A36 https://doi.org/10.1051/0004-6361/201015855 CrossRef Search ADS Martín-Doménech R. , Rivilla V. , Jiménez-Serra I. , Quénard D. , Testi L. , Martín-Pintado J. , 2017 , MNRAS , 469 , 2230 CrossRef Search ADS Martín-Pintado J. , de Vicente P. , Fuente A. , Planesas P. , 1997 , ApJ , 482 , L45 https://doi.org/10.1086/310691 CrossRef Search ADS Mendoza E. , Lefloch B. , López-Sepulcre A. , Ceccarelli C. , Codella C. , Boechat-Roberty H. M. , Bachiller R. , 2014 , MNRAS , 445 , 151 https://doi.org/10.1093/mnras/stu1718 CrossRef Search ADS Mendoza E. et al. , 2018 , MNRAS , 475 , 5501 CrossRef Search ADS Millen D. J. , Topping G. , Lide D. R. , 1962 , J. Mol. Spectrosc. , 8 , 153 https://doi.org/10.1016/0022-2852(62)90016-4 CrossRef Search ADS Minh Y. C. , Irvine W. M. , Ohishi M. , Ishikawa S. , Saito S. , Kaifu N. , 1993 , A&A , 267 , 229 Morris M. , Serabyn E. , 1996 , ARA&A , 34 , 645 https://doi.org/10.1146/annurev.astro.34.1.645 CrossRef Search ADS Müller H. S. P. , Thorwirth S. , Roth D. A. , Winnewisser G. , 2001 , A&A , 370 , L49 https://doi.org/10.1051/0004-6361:20010367 CrossRef Search ADS Müller H. S. P. , Schlöder F. , Stutzki J. , Winnewisser G. , 2005 , J. Mol. Struct , 742 , 215 https://doi.org/10.1016/j.molstruc.2005.01.027 CrossRef Search ADS Müller H. S. P. , Drouin B. J. , Pearson J. C. , 2009 , A&A , 506 , 1487 https://doi.org/10.1051/0004-6361/200912932 CrossRef Search ADS Neill J. L. et al. , 2014 , ApJ , 789 , 8 https://doi.org/10.1088/0004-637X/789/1/8 CrossRef Search ADS Oberg K. I. , Bottinelli S. , Jorgensen J. K. , van Dishoeck E. F. , 2010 , ApJ , 716 , 825 https://doi.org/10.1088/0004-637X/716/1/825 CrossRef Search ADS Ohishi M. , Kaifu N. , 1998 , Faraday Discuss. , 109 , 205 https://doi.org/10.1039/a801058g CrossRef Search ADS Ohishi M. , Suzuki T. , Hirota T. , Saito M. , Kaifu N. , 2017 , preprint (arXiv:1708.06871) Pickett H. M. , Poynter R. L. , Cohen E. A. , Delitsky M. L. , Pearson J. C. , Müller H. S. P. , 1998 , J. Quant. Spectrosc. Radiat. Transfer , 60 , 883 https://doi.org/10.1016/S0022-4073(98)00091-0 CrossRef Search ADS Quénard D. , Vastel C. , Ceccarelli C. , Hily-Blant P. , Lefloch B. , Bachiller R. , 2017a , MNRAS , 470 , 3194 CrossRef Search ADS Quénard D. , Jiménez-Serra I. , Viti S. , Holdship J. , Coutens A. , 2017b , MNRAS , 474 , 2796 CrossRef Search ADS Requena-Torres M. A. , Martín-Pintado J. , Rodríguez-Franco A. , Martín S. , Rodríguez-Fernández N. J. , de Vicente P. , 2006 , A&A , 455 , 971 https://doi.org/10.1051/0004-6361:20065190 CrossRef Search ADS Requena-Torres M. A. , Martón-Pintado J. , Martín S. , Morris M. R. , 2008 , ApJ , 672 , 352 https://doi.org/10.1086/523627 CrossRef Search ADS Rivilla V. M. , Fontani F. , Beltrán M. T. , Vasyunin A. , Caselli P. , Martín-Pintado J. , Cesaroni R. , 2016 , ApJ , 826 , 161 https://doi.org/10.3847/0004-637X/826/2/161 CrossRef Search ADS Rivilla V. M. , Beltrán M. T. , Cesaroni R. , Fontani F. , Codella C. , Zhang Q. , 2017 , A&A , 598 , A59 https://doi.org/10.1051/0004-6361/201628373 CrossRef Search ADS Rivilla V. M. et al. , 2018 , MNRAS , 475 , L30 https://doi.org/10.1093/mnrasl/slx208 CrossRef Search ADS Rodríguez-Fernández N. J. , Martín-Pintado J. , de Vicente P. , Fuente A. , Huettemeister S. , Wilson T. L. , Kunze D. , 2000 , A&A , 356 , 695 Rodríguez-Fernández N. J. , Martín-Pintado J. , Fuente A. , de Vicente P. , Wilson T. L. , Hüttemeister S. , 2001 , A&A , 365 , 174 https://doi.org/10.1051/0004-6361:20000020 CrossRef Search ADS Rodríguez-Fernández N. J. , Martín-Pintado J. , Fuente A. , Wilson T. L. , 2004 , A&A , 427 , 217 https://doi.org/10.1051/0004-6361:20041370 CrossRef Search ADS Rodríguez-Fernández N. , Tafalla M. , Gueth F. , Bachiller R. , 2010 , A&A , 516 , A98 https://doi.org/10.1051/0004-6361/201013997 CrossRef Search ADS Smith I. W. M. , Herbst E. , Chang Q. , 2004 , MNRAS , 350 , 323 https://doi.org/10.1111/j.1365-2966.2004.07656.x CrossRef Search ADS Sutton E. C. , Peng R. , Danchi W. C. , Jaminet P. A. , Sandell G. , Russell A. P. G. , 1995 , A&AS , 97 , 455 https://doi.org/10.1086/192147 Takakuwa S. , Kawaguchi K. , Mikami H. , Saito M. , 2001 , PASJ , 53 , 251 https://doi.org/10.1093/pasj/53.2.251 CrossRef Search ADS Takano S. et al. , 1998 , A&A , 329 , 1156 Taniguchi K. , Saito M. , Ozeki H. , 2016 , ApJ , 830 , 106 https://doi.org/10.3847/0004-637X/830/2/106 CrossRef Search ADS Taniguchi K. , Ozeki H. , Saito M. , 2017 , ApJ , 846 , 46 CrossRef Search ADS Taquet V. , Ceccarelli C. , Kahane C. , 2012 , A&A , 538 , A42 https://doi.org/10.1051/0004-6361/201117802 CrossRef Search ADS Theule P. , Borget F. , Mispelaer F. , Danger G. , Duvernay F. , Guillemin J. C. , Chiavassa T. , 2011 , A&A , 534 , A64 https://doi.org/10.1051/0004-6361/201117494 CrossRef Search ADS van Dishoeck E. F. , Blake G. A. , Jansen D. J. , Groesbeck T. D. , 1995 , ApJ , 447 , 760 https://doi.org/10.1086/175915 CrossRef Search ADS Vastel C. , Ceccarelli C. , Lefloch B. , Bachiller R. , 2014 , ApJ , 795 , L2 https://doi.org/10.1088/2041-8205/795/1/L2 CrossRef Search ADS Vastel C. , Ceccarelli C. , Lefloch B. , Bachiller R. , 2016 , A&A , 591 , L2 https://doi.org/10.1051/0004-6361/201628427 CrossRef Search ADS Vastel C. , Kawaguchi K. , Quénard D. , Ohishi M. , Lefloch B. , Bachiller R. , Müller H. S. P. , 2018 , MNRAS , 474 , L76 https://doi.org/10.1093/mnrasl/slx197 CrossRef Search ADS Vasyunin A. I. , Herbst E. , 2013 , ApJ , 769 , 34 https://doi.org/10.1088/0004-637X/769/1/34 CrossRef Search ADS Vasyunin A. I. , Caselli P. , Dulieu F. , Jiménez-Serra I. , 2017 , ApJ , 842 , 33 CrossRef Search ADS Yamaguchi T. et al. , 2011 , PASJ , 63 , L37 https://doi.org/10.1093/pasj/63.5.L37 CrossRef Search ADS Yu N. , Xu J. , Wang J.-J. , 2017 , Res. Astron. Astrophys. , 18 , 015 CrossRef Search ADS Yusef-Zadeh F. et al. , 2013 , ApJ , 762 , 33 https://doi.org/10.1088/0004-637X/762/1/33 CrossRef Search ADS Ziurys L. M. , McGonagle D. , Minh Y. , Irvine W. M. , 1991 , ApJ , 373 , 535 https://doi.org/10.1086/170072 CrossRef Search ADS APPENDIX A: MOLECULAR SPECTROSCOPY A1 Methyl cyanide CH3CN CH3CN is a strongly prolate symmetric tops molecule, whose rotational energy levels can be described by total angular momentum, J, and its projection along the symmetry axis, K. The permitted radiative transitions according to the selection rules are only within K ladders i.e. ΔK = 0. Therefore, successive K-components of a particular J → (J − 1) transition are expected to be present simultaneously in a narrow frequency band. In addition, the presence of the identical hydrogen nuclei gives rise to two symmetry species, distinguished by their nuclear spin state, denoted A- and E-state. Energy levels with K= 3n belong to A-states while E-states are referred to K ≠ 3n with n ≥ 0 (Boucher et al. 1977; Cazzoli & Puzzarini 2006; Müller, Drouin & Pearson 2009). In general, the statistical weight between A- and E-states are 2:1. However, if both states are formed in equilibrium conditions, the abundance ratios are expected to be 1 (Minh et al. 1993). A2 Cyanomethyl radical H2CCN and cyamamide NH2CN The radical CH2CN is the simplest cyanide derivative of the methyl radical, CH3. It has two interchangeable hydrogen nuclei with non-zero spin which dictate the existence of an ortho and para symmetry. The same occurs in the NCN-frame contained molecule, NH2CN. For both species, the quantum number Kais even for ortho levels and odd for para levels. The statistical weight of ortho:para is 3:1 due to their nuclear spin degeneracies, this means transitions of ortho levels would be three times stronger than equivalent transition of the para form (Millen, Topping & Lide 1962; Flower & Watt 1984; Takakuwa et al. 2001). A3 Methanimine CH2NH, vinl cyanide C2H3CN, ethyl cyanide C2H5CN, isocyanic acid HNCO, formamide NH2CHO, and methylamine CH3NH2, CH2NH is a near prolate planar asymmetric rotor, where the components of the electric dipole moment are constrained to lie along the a and b principal axes. This results in two types of allowed transitions in the rotational spectrum: a-type, in which the Ka (prolate) quantum number does not change and the Kb (oblate) quantum number changes by one unit (i.e. ΔKa= 0, ΔKb = ±1), and b-type, in which both Ka and Kc change by one unit (i.e. ΔKa = ±1, ΔKb = ±1), (Kirchhoff, Johnson & Lovas 1973; Dore et al. 2010; Dore, Bizzocchi & Degli Esposti 2012). C2H3CN and C2H5CN are planar asymmetric rotors with a- and b-type transitions, respectively, allowed in the rotational spectrum (Neill et al. 2014). All transitions detected in G+0.693 are stronger a-type spectra. HNCO and NH2CHO are simple asymmetric prolate rotors, hence a- and b-type transitions are allowed. For HNCO, only a-type transitions within the Ka = 0 and 1 ladders are detected. For NH2CHO, a-type transitions are observed from each Ka ladder up to Ka = 3 whilst only Ka = 0 and 1 ladders are observed in b-type transitions. CH3NH2 is a near-prolate asymmetric top molecule, whose a- and b-type transitions are allowed. In this case, both a-type and b-type transitions within the Ka = 0 and 1 ladders are detected. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
TI - Complex organic molecules in the Galactic Centre: the N-bearing family
JF - Monthly Notices of the Royal Astronomical Society
DO - 10.1093/mnras/sty1174
DA - 2018-05-04
UR - https://www.deepdyve.com/lp/oxford-university-press/complex-organic-molecules-in-the-galactic-centre-the-n-bearing-family-5UQueJRQXQ
SP - 1
EP - 2975
VL - Advance Article
IS - 3
DP - DeepDyve
ER -