Methods for the detection of stellar rotation periods in individual TESS sectors and results from the Prime mission

TL;DR

This study develops a robust method using machine learning to detect stellar rotation periods from TESS data, resulting in a large catalog of periods for FGKM dwarfs, aiding stellar evolution research.

Contribution

The paper introduces a novel approach combining light curve parameters and random forest classifiers to reliably detect stellar rotation periods in TESS sectors, expanding available data for stellar studies.

Findings

Detected 16,800 rotation periods from TESS data

Cataloged median periods for over 10,900 stars

Demonstrated effectiveness of machine learning in period detection

Abstract

For ongoing studies of the role of rotation in stellar evolution, we require large catalogs of rotation periods for testing and refining gyrochronology. While there is a wealth of data from the Kepler and K2 missions, TESS presents both an opportunity and a challenge: despite its all-sky coverage, rotation periods remain hard to detect. We analyzed individual TESS sectors to detect short-period stellar rotation, using only parameters measured from light curves for a robust and unbiased method of evaluating detections. We used random forest classifiers for vetting, trained on a large corpus of period measurements in KELT data from the Oelkers et al. (2018) catalog and using TESS full-frame image light curves generated by eleanor (Feinstein et al. 2019). Finally, using data from the first 26 sectors of TESS, we analyzed 432,704 2-minute cadence single-sector light curves for FGKM dwarfs. We detected 16,800 periods in individual sector light curves, covering 10,909 distinct targets, and we present a catalog of the median period for each target as measured by a Lomb-Scargle periodogram.