TESS Data for Asteroseismology (T'DA) Stellar Variability Classification Pipeline: Set-Up and Application to the Kepler Q9 Data

TL;DR

This paper presents an automated pipeline for classifying stellar variability in TESS data, validated on Kepler data, achieving high accuracy and enabling efficient processing of vast stellar observations.

Contribution

The paper introduces a new ensemble machine learning approach for automatic variability classification of TESS data, validated on Kepler observations, enhancing data analysis efficiency.

Findings

Achieved 94.9% accuracy on Kepler Q9 data

Successfully classified ~167,000 stars from Kepler Q9

Validated methodology for TESS data classification

Abstract

The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from $\sim$ 27 days to about 1 year of continuous observations. This vast amount of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labelled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all $\sim$ 167,000 stars observed in Q9 of the Kepler space mission.