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Principal Component Analysis (PCA) is being extensively used in Astronomy butnot yet exhaustively exploited for variability search. The aim of this work isto investigate the effectiveness of using the PCA as a method to search forvariable stars in large photometric data sets. We apply PCA to variabilityindices computed for light curves of 18152 stars in three fields in M 31extracted from the Hubble Source Catalogue. The projection of the data into theprincipal components is used as a stellar variability detection andclassification tool, capable of distinguishing between RR Lyrae stars, longperiod variables (LPVs) and non-variables. This projection recovered more than90% 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 candidatevariable stars.

Variability search in M 31 using Principal Component Analysis and the Hubble Source Catalog