Accurate Classification of Two-Dimensional Spectra with Artificial Neural Networks

Details Accurate Classification of Two-Dimensional Spectra with Artificial Neural Networks Accurate Classification of Two-Dimensional Spectra with Artificial Neural Networks

Dec 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995aas...187.1901w&link_type=abstract

American Astronomical Society, 187th AAS Meeting, #19.01; Bulletin of the American Astronomical Society, Vol. 27, p.1311

Astronomy and Astrophysics

Astronomy

Scientific paper

We have designed a suite of Artificial Neural Networks (ANNs) that are able to classify accurately the near-infrared spectra of ordinary stars in temperature and luminosity. The temperature classes O - M and luminosity classes Ia - V are classified in the extension of the MK System in the silicon-accessible (peak CCD and Reticon sensitivities) near-infrared (lambda lambda 5800 -- 8900). This classification system (Torres-Dodgen and Weaver, 1993, PASP, 105, 693) is rigorously based on MK standards. Although we employ only 15 Angstroms resolution spectra, the ANNs achieve fractional sub-class temperature and luminosity accuracies comparable to those obtained by expert classifiers with 2 Angstroms resolution photographic spectra. Even OB stars are well classified by this system. We have demonstrated previously (Weaver and Torres-Dodgen, 1995, Ap.J. 446, 300 and references therein) that ANN classification can detect, and classify the components of, binary stars; can accurately determine interstellar reddening during classification; and degrades slowly with decreasing signal-to-noise ratios. The remaining issue is to determine the limit of spectral peculiarities that can be detected by ANNs at these resolutions. Such ANNs can be used for automated spectroscopic surveys that are as accurate but two magnitudes deeper than spectroscopic surveys using expert human classifiers.

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