2018/03/23 by M. I. Moretti, D. Hatzidimitriou, A. Karampelas +5
Engineering · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Astronomical Observations and Instrumentation #Astronomy #Astronomy and Astrophysical Research #Astrophysics #Computer science #Globular cluster #Light curve #Mathematics #Pattern recognition (psychology) #Physics #Principal component analysis #Projection (relational algebra) #RR Lyrae variable #Stars #Stellar, planetary, and galactic studies #Variable star #astro-ph.GA #astro-ph.IM #astro-ph.SR
paper · pdf · doi:10.1093/mnras/sty758
23 pages, 18 figures, MNRAS accepted
openalex publication_date 2018/03/23 · arxiv created 2018/03/26 · openalex created_date 2018/03/29 · arxiv updated 2018/04/04 · openalex updated_date 2026/08/05
Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long period variables (LPVs) and non-variables. This projection recovered more than 90% of the known variables and revealed 38 previously unknown variable stars (about 30% more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.