Classification Of Stellar Spectra Research Articles

Near‐Infrared Classification Spectroscopy:J‐Band Spectra of Fundamental MK Standards

We present a catalog of J-band spectra for 88 fundamental MK standard stars observed at a resolving power of R ~ 3000. This contribution serves as a companion atlas to the K-band spectra published by Wallace and Hinkle (1997) and the H-band atlas of Meyer et al. (1998). We report data from 7400-9550 1/cm (1.05-1.34 microns) for stars of spectral types O7-M6 and luminosity classes I-V as defined in the MK system. In reducing these data, special care has been taken to remove time-variable telluric features of water vapor. We identify atomic and molecular indices which are both temperature and luminosity sensitive that aid in the classification of stellar spectra in the J-band. In addition to being useful in the classification of late-type stars, the J-band contains several features of interest in the study of early-type stellar photospheres. These data are available electronically for anonymous FTP in addition to being served on the World Wide Web.

Automated classification of stellar spectra -- II. Two-dimensional classification with neural networks and principal components analysis

We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the Michigan Spectral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (sig_68 = 0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types present in the data set (B2-M7). We show also that the networks yield correct luminosity classes for over 95% of both dwarfs and giants with a high degree of confidence. Stellar spectra generally contain a large amount of redundant information. We investigate the application of Principal Components Analysis (PCA) to the optimal compression of spectra. We show that PCA can compress the spectra by a factor of over 30 while retaining essentially all of the useful information in the data set. Furthermore, it is shown that this compression optimally removes noise and can be used to identify unusual spectra.

Accurate Two‐Dimensional Classification of Stellar Spectra with Artificial Neural Networks

We present a solution to the long-standing problem of automatically classifying stellar spectra of all temperature and luminosity classes with the accuracy shown by expert human classifiers. We use the 15 A resolution near-infrared spectral classification system described by Torres-Dodgen & Weaver in 1993. Using the spectrum with no manual intervention except wavelength registration, artificial neural networks (ANNs) can classify these spectra with Morgan-Keenan types with an accuracy comparable to that obtained by human experts using 2 A resolution blue spectra, which is about 0.5 types (subclasses) in temperature and about 0.25 classes in luminosity. Accurate temperature classification requires a hierarchy of ANNs, while luminosity classification is most successful with a single ANN. We propose an architecture for a fully automatic classification system.

WILLIAM WILSON MORGAN (1906-1994)

ABSTRACT W. W. Morgan was a Professor of Astronomy at the University of Chicago and a key figure in the conception and development of the philosophy and practice of morphological classification techniques in Twentieth Century astronomy. His primary contributions were development of the modern classification of stellar spectra and isolation of peculiar stars, delineation of the spiral structure of the Milky Way Galaxy, and the Yerkes system of galaxy classification.

Automating the Classification of Stellar Spectra

We are automating the multi-parameter classification of stellar spectra within the MK system. The increased rate at which spectra are gathered by increasingly larger telescopes, fibre systems and efficient CCD detectors necessitates the need for automation of stellar classification. As well as greatly speeding up this process, an automated scheme would give reproducible results, would arguably be more objective, and would give quick and useful reporting of classification uncertainties.

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-A resolution from ≈ 3850 to 5150 A. 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.

Spectral classification with principal component analysis and artificial neural networks

Derived from non-linear signal processing strategies common to biological systems, neural network algorithms generalise classical data analysis techniques, e.g. Fourier analysis, Wiener filtering, and vector clustering algorithms. Conversely, multifactor analysis tools such as principal component analysis can function in a manner analogous to that of an unsupervised neural network. We have explored the use of principal component analysis for data pre-processing prior to classification of stellar spectra with a non-linear neural network. The strategy significantly enhances classification replicability, network stability, and convergence.

Classification of Stellar Spectra

SummaryThe objectives of this exercise are to gain familiarity with the appearance of the spectra of stars, to understand some of the temperature and luminosity criteria used to classify spectra, and to gain insights into the relationship between the appearance of stellar spectra and physical conditions in stellar atmospheres.

Two Dimensional Spectral Classification Using a Computer

AbstractA computer program for the automatic classification of stellar spectra is described. The aim of this program is to derive accurate absolute luminosities and spectral classes of individual stars for improved spectral parallaxes. Preliminary results are presented and future applications are discussed.

A New Classification of Stellar Spectra

MANY of the recent advances in our knowledge of the constitution of the stars are traceable to Prof. Pickering's revival of Fraunhofer's mode of investigating stellar spectra. The endowment of this research by Mrs. Draper as a memorial to her husband, Dr. Henry Draper, has enabled Prof. Pickering to apply this method in two principal directions. First, a series of photographs was taken on a small scale to indicate the chief characteristics of the spectra of a very great number of stars; second, in the case of the brighter stars, another series was taken with greater dispersion with the view of facilitating an inquiry into the more minute features of each type of spectrum. The results of the first investigation are comprised in the well-known “Draper Catalogue,” giving particulars of the spectra of over 10,000 stars (NATURE, vol. xlv. p. 427), and the research has now been advanced another stage by the publication of the results obtained along the second line of inquiry.

CLASSIFICATION OF STELLAR SPECTRA

CLASSIFICATION OF STELLAR SPECTRA