Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis

TL;DR

This paper introduces a PCA-based method to enhance the detection and classification of variable stars in large survey data, successfully identifying new high-amplitude Delta Scuti and RR Lyrae stars in SDSS Stripe 82.

Contribution

It presents a novel PCA framework that improves period detection and enables efficient classification of variable stars in multi-band photometric surveys.

Findings

Identified 132 high-amplitude Delta Scuti variables.

Discovered 129 new RR Lyrae stars, extending the halo mapping.

Detected 25 multiperiodic or Blazhko RR Lyrae stars.

Abstract

We propose a robust principal component analysis (PCA) framework for the exploitation of multi-band photometric measurements in large surveys. Period search results are improved using the time series of the first principal component due to its optimized signal-to-noise ratio.The presence of correlated excess variations in the multivariate time series enables the detection of weaker variability. Furthermore, the direction of the largest variance differs for certain types of variable stars. This can be used as an efficient attribute for classification. The application of the method to a subsample of Sloan Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti variables. We found also 129 new RR Lyrae variables, complementary to the catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR Lyrae stars towards the Galactic bulge. The sample comprises also 25 multiperiodic or Blazhko RR Lyrae stars.