Automated classification of periodic variable stars{Improved methodology for the automated classification of periodic variable stars}

TL;DR

This paper introduces a new automated Bayesian-based method for detecting and classifying periodic variable stars in large photometric datasets, successfully identifying various types including binaries and pulsators.

Contribution

It presents a novel multi-stage Bayesian methodology for automated classification of periodic variable stars in extensive photometric data.

Findings

Successfully applied to ~26,000 stars in the TrES Lyr1 field.

Detected diverse types of variable stars including binaries and pulsators.

Demonstrated effectiveness of the Bayesian approach in large-scale data analysis.

Abstract

We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and use a multi-stage approach. We applied our method to the ground-based data of the TrES Lyr1 field, which is also observed by the Kepler satellite, covering ~26000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, Gamma Doradus, Beta Cephei and Delta Scuti stars. Also a few classical radial pulsators were found.