Efficient Use of Simultaneous Multi-Band Observations for Variable Star Analysis
The luminosity changes of most types of variable stars are correlated in different wavelengths, and these correlations may be exploited for several purposes: variability detection, distinguishing microvariability from noise, period searches, or classification. Principal component analysis (PCA) 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 magnitudes. These particularities require robust methods to be applied together with PCA. The results show that PCA is a valuable aid in variability analysis with multiband data.
KeywordsPrincipal Component Analysis Point Cloud Variable Star Period Search Robust Principal Component Analysis
- 1.Adelman-McCarthy JK et al (2008) Astrophys J Suppl S 175:297Google Scholar
- 3.Breiman L (2001) Machine Learning 45:5Google Scholar
- 10.Rousseeuw PJ (1985) Mathematical statistics and applications. In: Grossmann W, Pflug G, Vincze I, Wertz W (eds), vol. B. Reidel, Dordrecht, p 28Google Scholar