TY - JOUR
TI - Variability search in M31 using principal component analysis and the Hubble Source Catalogue
AU - Moretti, M.I.
AU - Hatzidimitriou, D.
AU - Karampelas, A.
AU - Sokolovsky, K.V.
AU - Bonanos, A.Z.
AU - Gavras, P.
AU - Yang, M.
JO - Monthly Notices of the Royal Astronomical Society
PY - 2018
VL - 477
TODO - 2
SP - 2664-2683
PB - Oxford University Press
SN - 0035-8711, 1365-2966
TODO - 10.1093/MNRAS/STY758
TODO - null
TODO - 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 18 152 stars in three fields inM31 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 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type.We conclude that this methodology can indeed successfully identify candidate variable stars. © 2017 The Authors.
ER -