From NBODY1 to NBODY6: The Growth of an IndustryBrouwer Award Lecture.Aarseth, Sverre J.
doi: 10.1086/316455pmid: N/A
I review the development of direct N‐body codes at Cambridge over nearly 40 years, highlighting the main stepping stones. The first code (NBODY1) was based on the simple concepts of a force polynomial combined with individual time steps, where numerical problems due to close encounters were avoided by a softened potential. Fortuitously, the elegant Kustaanheimo‐Stiefel two‐body regularization soon permitted small star clusters to be studied (NBODY3). Subsequent extensions to unperturbed three‐body and four‐body regularization proved beneficial in dealing with multiple interactions. Investigations of larger systems became possible with the Ahmad‐Cohen neighbor scheme which was used more than 20 years ago for expanding universe models of 4000 galaxies (NBODY2). Combining the neighbor scheme with the regularization procedures enabled more realistic star clusters to be considered (NBODY5). After a period of simulations with no apparent technical progress, chain regularization replaced the treatment of compact subsystems (NBODY3, NBODY5). More recently, the Hermite integration method provided a major advance and has been implemented on the special‐purpose HARP computers (NBODY4) together with an alternative version for workstations and supercomputers (NBODY6). These codes also include a variety of algorithms for stellar evolution based on fast lookup functions. The treatment of primordial binaries contains efficient procedures for chaotic two‐body motion as well as tidal circularization, and special attention is paid to hierarchical systems and their stability. This family of N‐body codes constitutes a powerful tool for dynamical simulations which is freely available to the astronomical community, and the massive effort owes much to collaborators.
On the Reliability of Cross‐Correlation Function Lag Determinations in Active Galactic NucleiWelsh, W. F.
doi: 10.1086/316457pmid: N/A
Many active galactic nuclei (AGNs) exhibit a highly variable luminosity. Some AGNs also show a pronounced time delay between variations seen in their optical continuum and in their emission lines. In effect, the emission lines are light echoes of the continuum. This light‐travel time delay provides a characteristic radius of the region producing the emission lines. The cross‐correlation function (CCF) is the standard tool used to measure the time lag between the continuum and line variations. For the few well‐sampled AGNs, the lag is ∼1–100 days, depending upon which line is used and the luminosity of the AGN. In the best sampled AGN, NGC 5548, the Hβ lag shows year‐to‐year changes, ranging from ∼8.7 to ∼22.9 days over a span of 8 years. In this paper it is demonstrated that, in the context of AGN variability studies, the lag estimate using the CCF is biased too low and subject to a large variance. Thus the year‐to‐year changes of the measured lag in NGC 5548 do not necessarily imply changes in the AGN structure. The bias and large variance are consequences of finite‐duration sampling and the dominance of long timescale trends in the light curves, not of noise or irregular sampling. Lag estimates can be substantially improved by removing low‐frequency power from the light curves prior to computing the CCF.
The Early Palomar Program (1950–1955) for the Discovery of Classical Novae in M81: Analysis of the Spatial Distribution, Magnitude Distribution, and Distance SuggestionShara, Michael M.; Sandage, Allan; Zurek, David R.
doi: 10.1086/316449pmid: N/A
Data obtained in the 1950–1955 Palomar campaign for the discovery of classical novae in M81 are set out in detail. Positions and apparent B magnitudes are listed for the 23 novae that were found. There is modest evidence that the spatial distribution of the novae does not track the B brightness distribution of either the total light or the light beyond an isophotal radius that is 70″ from the center of M81. The nova distribution is more extended than the aforementioned light, with a significant fraction of the sample appearing in the outer disk/spiral arm region. We suggest that many (perhaps a majority) of the M81 novae that are observed at any given epoch (compared with, say, 1010 years ago) are daughters of Population I interacting binaries. The conclusion that the present‐day novae are drawn from two population groups—one from low‐mass white dwarf secondaries of close binaries identified with the bulge/thick disk population, and the other from massive white dwarf secondaries identified with the outer thin disk/spiral arm population—is discussed. We conclude that the M81 data are consistent with the two population division as argued previously from (1) observational studies on other grounds of nearby galaxies, (2) Monte Carlo simulations of novae in M31 and in the Galaxy, and (3) population synthesis modeling of nova binaries. Two different methods of using M81 novae as distance indicators give a nova distance modulus for M81 as (m−M)0 = 27.75, consistent with the Cepheid modulus that is the same value.
Faint Field Galaxies around Bright Stars: A New Strategy for Imaging at the Diffraction LimitLarkin, J. E.; Glassman, T. M.
doi: 10.1086/316448pmid: N/A
This paper presents a new strategy for observing faint galaxies with high‐order natural guide star systems. We have imaged five high Galactic latitude fields within the isoplanatic patch of bright stars (8.5 mag < R < 10.3 mag). The fields provide a rich set of faint field galaxies that are observable with a natural guide star adaptive optics (AO) system on a large telescope. Because of the small fields of many AO science cameras, these preliminary images are necessary to identify candidate galaxies. We present the photometry and positions for 78 objects (at least 40 galaxies) near five bright stars, appropriate for diffraction‐limited studies with the Keck and other AO systems on large ground‐based telescopes. The K‐band seeing conditions in each field were excellent (0.″4–0.″7), allowing us to identify stars and estimate galaxy sizes. We also simulate AO images of field galaxies to determine the feasibility of infrared morphological studies at the diffraction limit. With new high‐order AO systems coming on line with 8–10 m class telescopes, we believe these observations are invaluable in beginning to study faint galaxy populations at the diffraction limit.
Spectrophotometry: Revised Standards and TechniquesBessell, Michael S.
doi: 10.1086/316454pmid: N/A
The telluric features redward of 6700 Å have been removed from the accurate spectrophotometric standards of Hamuy et al. to permit more reliable relative and absolute spectrophotometry to be obtained from CCD spectra. Smooth fluxes from 3300 to 10500 Å are best determined by dividing the raw spectra of all objects taken in a night by the raw spectrum of a “smooth” spectrum star before deriving the instrumental response function using the revised standard star fluxes. In this way the telluric features and any large instrumental variation with wavelength are removed from the raw data, leaving smooth spectra that need only small corrections to place them on an absolute flux scale. These small corrections with wavelength are well described by a low‐order polynomial and result in very smooth flux‐calibrated spectra.
The Photometry of Undersampled Point‐Spread FunctionsLauer, Tod R.
doi: 10.1086/316460pmid: N/A
An undersampled point‐spread function (PSF) may interact with the microstructure of a solid‐state detector such that the total flux detected can depend sensitively on where the PSF center falls within a pixel. Such intrapixel sensitivity variations will not be corrected by flat‐field calibration and may limit the accuracy of stellar photometry conducted with undersampled images, as are typical for Hubble Space Telescope observations. The total flux in a stellar image can vary by up to 0.03 mag in F555W WFC images depending on how it is sampled, for example. For NIC3, these variations are especially strong, up to 0.39 mag, strongly limiting its use for stellar photometry. Intrapixel sensitivity variations can be corrected for, however, by constructing a well‐sampled PSF from a dithered data set. The reconstructed PSF is the convolution of the optical PSF with the pixel response. It can be evaluated at any desired fractional pixel location to generate a table of photometric corrections as a function of relative PSF centroid. A caveat is that the centroid of an undersampled PSF can also be affected by the pixel response function; thus sophisticated centroiding methods, such as cross‐correlating the observed PSF with its fully sampled counterpart, are required to derive the proper photometric correction.