Efficient use of simultaneous multi-band observations for variable star analysis

Abstract

The luminosity changes of most types of variable stars are correlated in the different wavelengths, and these correlations may be exploited for several purposes: for variability detection, for distinction of microvariability from noise, for period search or for classification. Principal component analysis is a simple and well-developed statistical tool to analyze correlated data. We will discuss its use on variable objects of Stripe 82 of the Sloan Digital Sky Survey, with the aim of identifying new RR Lyrae and SX Phoenicis-type candidates. The application is not straightforward because of different noise levels in the different bands, the presence of outliers that can be confused with real extreme observations, under- or overestimated errors and the dependence of errors on the magnitudes. These particularities require robust methods to be applied together with the principal component analysis. The results show that PCA is a valuable aid in variability analysis with multi-band data.