Automatic classification of K2 pulsating stars using machine learning techniques

TL;DR

This paper presents a machine learning approach using Random Forest to automatically classify K2 pulsating stars into four categories, achieving over 80% accuracy, thereby aiding in the analysis of large stellar datasets.

Contribution

The study introduces a novel automated classification method for K2 pulsating stars using machine learning, incorporating stellar features like temperature, luminosity, and FliPer metrics.

Findings

Over 80% classification accuracy achieved

Effective features include temperature, luminosity, and FliPer

Method facilitates large-scale stellar classification

Abstract

The second mission of the NASA Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars.