Wb. Weaver et Av. Torresdodgen, NEURAL-NETWORK CLASSIFICATION OF THE NEAR-INFRARED SPECTRA OF A-TYPE STARS, The Astrophysical journal, 446(1), 1995, pp. 300-317
We present an atlas of near-infrared (NIR) spectra of A stars for lumi
nosity classes Ia through V in the 15 Angstrom resolution system descr
ibed by Torres-Dodgen and Weaver (1993) and demonstrate an accurate me
thod to automatically classify A stars on this system. Using equivalen
t widths, artificial neural networks (ANNs) can classify these spectra
to an accuracy of 0.4 types (subclasses) in temperature and 0.15 clas
ses in luminosity. Using the spectrum, with no manual intervention exc
ept wavelength registration, ANNs can classify these spectra with an a
ccuracy comparable to that of 2 Angstrom resolution MK classification:
0.5 types in temperature and 0.35 classes in luminosity. In addition,
ANNs can concurrently determine reddening to an accuracy of 0.05 in E
(B-V). We demonstrate that this NIR-ANN spectral classification system
has the primary properties needed for automated classification survey
s: it is based in the most efficient spectral region of modern silicon
-based detectors, it requires low-resolution (15 Angstrom) spectra to
achieve sub-classification box accuracy, it can produce two-dimensiona
l classifications at least as accurate as those by expert human classi
fiers, it is relatively insensitive to interstellar reddening and can
accurately determine the reddening, it can identify and classify compo
site spectra, it degrades slowly with decreasing signal-to-noise ratio
, and it requires a minimum of human interaction at all stages of the
process.