Discrete Classification with Principal Component Analysis: Discrimination of Giant and Dwarf Spectra in K Stars

Abstract

view Abstract Citations (15) References (23) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Discrete Classification with Principal Component Analysis: Discrimination of Giant and Dwarf Spectra in K Stars Ibata, Rodrigo A. ; Irwin, Michael J. Abstract We demonstrate the use of a variant of Principal Component Analysis (PCA) for discrimination problems in astronomy. This variant of PCA is shown to provide the best linear discrimination between data classes. As a test case, we present the problem of discrimination between K giant and K dwarf stars from intermediate resolution spectra near the Mg `b' feature. The discrimination procedure is trained on a set of 24 standard K giants and 24 standard K dwarfs, and then used to perform giant - dwarf classification on a sample of ~ 1500 field K stars of unknown luminosity class which were initially classified visually. For the highest S/N spectra, the automated classification agrees very well (at the 90 - 95% level) with the visual classification. Most importantly, however, the automated method is found to classify stars in a repeatable fashion, and, according to numerical experiments, is very robust to signal to noise (S/N) degradation. Publication: The Astronomical Journal Pub Date: May 1997 DOI: 10.1086/118398 arXiv: arXiv:astro-ph/9704014 Bibcode: 1997AJ....113.1865I Keywords: Astrophysics E-Print: 16 pages, 4 figures, 2 tables. Accepted for publication in AJ full text sources arXiv | ADS | data products SIMBAD (26)