Automated classification of stellar spectra - I. Initial results with artificial neural networks

We have initiated a project to classify stellar spectra automatically from high-dispersion objective prism plates. The automated technique presented here is a simple backpropagation neural network, and is based on the visual classification work of Houk. The plate material (Houk's) is currently being digitized, and contains ≈ 105 stars down to V ≈ 11 at ≈ 2-Å resolution from ≈ 3850 to 5150 Å. For this first paper in the series we report on the results of 575 stars digitized from 6 plates. We find that even with the limited data set now in hand we can determine the temperature classification to better than 1.7 spectral subtypes from B3 to M4. Our current sample size provides insufficient training set material to generate luminosity and metallicity classifications. Our eventual aims in this project are (1) to create a large and homogeneous digital stellar spectral library; (2) to create a well-understood and robust automatic classification algorithm which can determine temperatures, luminosities and metallicities for a wide variety of spectral types; (3) to use these data, supplemented by deeper plate material, for the study of Galactic structure and chemical evolution; and (4) to find unusual or new classes of objects

Morphological Classification of galaxies by Artificial Neural Networks

We explore a method for automatic morphological classification of galaxies by an Artificial Neural Network algorithm. The method is illustrated using 13 galaxy parameters measured by machine (ESO-LV), and classified into five types (E, S0, Sa + Sb, Sc + Sd and Irr). A simple Backpropagation algorithm allows us to train a network on a subset of the catalogue according to human classification, and then to predict, using the measured parameters, the classification for the rest of the catalogue. We show that the neural network behaves in our problem as a Bayesian classifier, i.e. it assigns the a posteriori probability for each of the five classes considered. The network highest probability choice agrees with the catalogue classification for 64 percent of the galaxies. If either the first or the second highest probability choice of the network is considered, the success rate is 90 per cent. The technique allows uniform and more objective classification of very large extragalactic data sets

AUTOMATED MORPHOLOGICAL CLASSIFICATION OF APM GALAXIES BY SUPERVISED ARTIFICIAL NEURAL NETWORKS

We train Artificial Neural Networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms are extracted, all in a fully automated manner. The galaxy sample was first classified by 6 independent experts. We use several definitions for the mean type of each galaxy, based on those classifications. We then train and test the network on these features. We find that the rms error of the network classifications, as compared with the mean types of the expert classifications, is 1.8 Revised Hubble Types. This is comparable to the overall rms dispersion between the experts. This result is robust and almost completely independent of the network architecture used.Comment: The full paper contains 25 pages, and includes 22 figures. It is available at ftp://ftp.ast.cam.ac.uk/pub/hn/apm2.ps . The table in the appendix is available on request from [email protected]. Mon. Not. R. Astr. Soc., in pres

The evolution of Omega(HI) and the epoch of formation of damped Lyman-alpha absorbers

We present a study of the evolution of the column density distribution, f(N,z), and total neutral hydrogen mass in high-column density quasar absorbers using candidates from a recent high-redshift survey for damped Lyman-alpha (DLA) and Lyman limit system (LLS) absorbers. The observed number of LLS (N(HI)> 1.6 * 10^{17} atom/cm^2) is used to constrain f(N,z) below the classical DLA Wolfe et al. (1986) definition of 2 * 10^{20} atom/cm^2. The joint LLS-DLA analysis shows unambiguously that f(N,z) deviates significantly from a single power law and that a Gamma-law distribution of the form f(N,z)=(f_*/N_*)(N/N_*)^{-Beta} exp(-N/N_*) provides a better description of the observations. These results are used to determine the amount of neutral gas contained in DLAs and in systems with lower column density. Whilst in the redshift range 2 to 3.5, ~90% of the neutral HI mass is in DLAs, we find that at z>3.5 this fraction drops to only 55% and that the remaining 'missing' mass fraction of the neutral gas lies in sub-DLAs with N(HI) 10^{19} - 2 * 10^{20} atom/cm^2. The characteristic column density, N_*, changes from 1.6 * 10^{21} atom/cm^2 at z3.5, supporting a picture where at z>3.5, we are directly observing the formation of high column density neutral hydrogen DLA systems from lower column density units. Moreover since current metallicity studies of DLA systems focus on the higher column density systems they may be giving a biased or incomplete view of global galactic chemical evolution at z>3. After correcting the observed mass in HI for the ``missing'' neutral gas the comoving mass density now shows no evidence for a decrease above z=2. (abridged)Comment: Replaced to match version published in MNRAS. One figure and appendix added, analysis and conclusions unchange

Chemical Abundances of the Damped Lya Systems at z>1.5

We present chemical abundance measurements for 19 damped lya systems observed with HIRES on the 10m W.M. Keck Telescope. Our principal goal is to investigate the abundance patterns of the damped systems and thereby determine the underlying physical processes which dominate their chemical evolution. We place particular emphasis on gauging the relative importance of two complementary effects often invoked to explain the damped lya abundances: (1) nucleosynthetic enrichment from Type II supernovae and (2) an ISM-like dust depletion pattern. Similar to the principal results of Lu et al. (1996), our observations lend support both for dust depletion and Type II SN enrichment. Specifically, the observed overabundance of Zn/Fe and underabundance of Ni/Fe relative to solar abundances suggest significant dust depletion within the damped lya systems. Meanwhile, the relative abundances of Al, Si, and Cr vs. Fe are consistent with both dust depletion and Type II supernova enrichment. Our measurements of Ti/Fe and the Mn/Fe measurements from Lu et al. (1996), however, cannot be explained by dust depletion and indicate an underlying Type II SN pattern. Finally, the observed values of [S/Fe] are inconsistent with the combined effects of dust depletion and the nucleosynthetic yields expected for Type II supernovae. This last result emphasizes the need for another physical process to explain the damped lya abundance patterns. We also examine the metallicity of the damped lya systems both with respect to Zn/H and Fe/H. Our results confirm previous surveys by Pettini and collaborators, i.e., [] = -1.15 +/- 0.15 dex. [abridged]Comment: 18 pages, 4 embedded figures, 20 additional figures. Accepted to the Astrophysical Journal 10/20/98. Uses Latex2e, emualteapj.sty, and onecolfloat.st

Neural computation as a tool for galaxy classification : methods and examples

We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets from the ESO-LV catalogue, for which both machine parameters and human classification are available. The main methods we explore are: (i) Principal Component Analysis (PCA) which tells how independent and informative the input parameters are. (ii) Encoder Neural Network which allows us to find both linear (PCA-like) and non-linear combinations of the input, illustrating an example of unsupervised ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton algorithms) based on a training set for which the human classification is known. Here the output for previously unclassified galaxies can be interpreted as either a continuous (analog) output (e.g. $T$-type) or a Bayesian {\it a posteriori} probability for each class. Although the ESO-LV parameters are sub-optimal, the success of the ANN in reproducing the human classification is 2 $T$-type units, similar to the degree of agreement between two human experts who classify the same galaxy images on plate material. We also examine the aspects of ANN configurations, reproducibility, scaling of input parameters and redshift information.Comment: uuencoded compressed postscript. The preprint is also available at http://www.ast.cam.ac.uk/preprint/PrePrint.htm

The DEEP2 Galaxy Redshift Survey: Redshift Identification of Single-Line Emission Galaxies

We present two methods for determining spectroscopic redshifts of galaxies in the DEEP2 survey which display only one identifiable feature, an emission line, in the observed spectrum ("single-line galaxies"). First, we assume each single line is one of the four brightest lines accessible to DEEP2: Halpha, [OIII] 5007, Hbeta, or [OII] 3727. Then, we supplement spectral information with BRI photometry. The first method, parameter space proximity (PSP), calculates the distance of a single-line galaxy to galaxies of known redshift in (B-R), (R-I), R, observed wavelength parameter space. The second method is an artificial neural network (ANN). Prior information, such as allowable line widths and ratios, rules out one or more of the four lines for some galaxies in both methods. Based on analyses of evaluation sets, both methods are nearly perfect at identifying blended [OII] doublets. Of the lines identified as Halpha in the PSP and ANN methods, 91.4% and 94.2% respectively are accurate. Although the methods are not this accurate at discriminating between [OIII] and Hbeta, they can identify a single line as one of the two, and the ANN method in particular unambiguously identifies many [OIII] lines. From a sample of 640 single-line spectra, the methods determine the identities of 401 (62.7%) and 472 (73.8%) single lines, respectively, at accuracies similar to those found in the evaluation sets.Comment: 11 pages, 6 figures, accepted to Ap

APM z >> 4 QSO survey: spectra and intervening absorption systems

The APM multicolor survey for bright z > 4 objects, covering 2500 deg^2 of sky to m(R)~19, resulted in the discovery of thirty-one quasars with z > 4. High signal-to-noise optical spectrophotometry at 5A resolution has been obtained for the twenty-eight quasars easily accessible from the northern hemisphere. These spectra have been surveyed to create new samples of high redshift Lyman-limit systems, damped Lyman-alpha absorbers, and metal absorption systems (e.g. CIV and MgII). In this paper we present the spectra, together with line lists of the detected absorption systems. The QSOs display a wide variety of emission and absorption line characteristics, with 5 exhibiting broad absorption lines and one with extremely strong emission lines (BR2248-1242). Eleven candidate damped Ly-alpha absorption systems have been identified covering the redshift range 2.83.5). An analysis of the measured redshifts of the high ionization emission lines with the low ionization lines shows them to be blueshifted by 430+/-60 km/s. In a previous paper (Storrie-Lombardi et. al. 1994) we discussed the redshift evolution of the Lyman limit systems catalogued here. In subsequent papers we will discuss the properties of the Ly-alpha forest absorbers and the redshift and column density evolution of the damped Ly-alpha absorbers

The evolution of Ω_(Hi) and the epoch of formation of damped Lyman α absorbers

We present a study of the evolution of the column density distribution, f(N, z), and total neutral hydrogen mass in high column density quasar absorbers using candidates from a recent high-redshift survey for damped Lyman α (DLA) and Lyman-limit system (LLS) absorbers. The observed number of LLS [N(H_i) >1.6 × 10^(17) atom cm^(−2)] is used to constrain f(N, z) below the classical DLA definition of 2 × 10^(20) atom cm^(−2). The evolution of the number density of LLS is consistent with our previous work but steeper than previously published work of other authors. At z= 5, the number density of Lyman-limit systems per unit redshift is ∼5, implying that these systems are a major source of ultraviolet (UV) opacity in the high-redshift Universe. The joint LLS–DLA analysis shows unambiguously that f(N, z) deviates significantly from a single power law and that a Γ-law distribution of the form f(N,z) = (f_*/N_*)(N/N_*)^(−β)exp(−N/N_*) provides a better description of the observations. These results are used to determine the amount of neutral gas contained in DLAs and in systems with lower column density. Whilst in the redshift range 2–3.5, ∼90 per cent of the neutral H i mass is in DLAs, we find that at z > 3.5 this fraction drops to only 55 per cent and that the remaining ‘missing’ mass fraction of the neutral gas lies in sub-DLAs with N(H i) 10^(19)–2 × 10^(20) atom cm^(−2). The characteristic column density, N_*, changes from 1.6 × 10^(21) atom cm^(−2) at z 3.5, supporting a picture where at z > 3.5, we are directly observing the formation of high column density neutral hydrogen DLA systems from lower column density units. Moreover, since current metallicity studies of DLA systems focus on the higher column density systems they may be giving a biased or incomplete view of global galactic chemical evolution at z > 3. After correcting the observed mass in H i for the ‘missing’ neutral gas the comoving mass density now shows no evidence for a decrease above z= 2

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl