Astraea: Predicting Long Rotation Periods with 27-Day Light Curves

Abstract

The rotation periods of planet-hosting stars can be used for modeling and mitigating the impact of magnetic activity in radial velocity measurements and can help constrain the high-energy flux environment and space weather of planetary systems. Millions of stars and thousands of planet hosts are observed with the Transiting Exoplanet Survey Satellite (TESS). However, most will only be observed for 27 contiguous days in a year, making it difficult to measure rotation periods with traditional methods. This is especially problematic for field M dwarfs, which are ideal candidates for exoplanet searches, but which tend to have periods in excess of the 27-day observing baseline. We present a new tool, Astraea, for predicting long rotation periods from short-duration light curves combined with stellar parameters from Gaia DR2. Using Astraea, we can predict the rotation periods from Kepler 4-year light curves with 13% uncertainty overall (and a 9% uncertainty for periods > 30 days). By training on 27-day Kepler light curve segments, Astraea can predict rotation periods up to 150 days with 9% uncertainty (5% for periods > 30 days). After training this tool on these 27-day Kepler light curve segments, we applied \texttt{Astraea} to real TESS data. For the 195 stars that were observed by both Kepler and TESS, we were able to predict the rotation periods with 55% uncertainty despite the wild differences in systematics.

References in corpus (12)

• The Gaia mission
• The Transiting Exoplanet Survey Satellite
• Planets and Stellar Activity: Hide and Seek in the CoRoT-7 system
• The Geneva-Copenhagen Survey of the Solar neighbourhood II. New uvby calibrations and rediscussion of stellar ages, the G dwarf problem, age-metallicity diagram, and heating mechanisms of the disk
• Inferring probabilistic stellar rotation periods using Gaussian processes
• The Evolution of Flare Activity with Stellar Age
• The age-velocity dispersion relation of the Galactic discs from LAMOST-Gaia data
• Flares in Open Clusters with K2. I. M45 (Pleiades), M44 (Praesepe) and M67
• Exploring the evolution of stellar rotation using Galactic kinematics
• A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves
• Rigorous treatment of barycentric stellar motion: Perspective and light-time effects in astrometric and radial velocity data
• Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks

Cited by in corpus (10)

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• Rotation Distributions around the Kraft Break with TESS and Kepler: The Influences of Age, Metallicity, and Binarity
• Three K2 Campaigns Yield Rotation Periods for 1013 Stars in Praesepe
• ROOSTER: a machine-learning analysis tool for Kepler stellar rotation periods
• Universal properties of the high- and low-α disk: small intrinsic abundance scatter and migrating stars
• Circumbinary planets: migration, trapping in mean-motion resonances, and ejection
• Predicting Stellar Rotation Periods Using XGBoost
• Theoretical and Observational Evidence for Coriolis Effects in Coronal Magnetic Fields Via Direct Current Driven Flaring Events
• TOI-1634 b: an Ultra-Short Period Keystone Planet Sitting Inside the M Dwarf Radius Valley