Gaussian Process Models Impact the Inferred Properties of Giant Planets around Active Stars

Abstract

The recent development of statistical methods that can distinguish between stellar activity and dynamical signals in radial velocity (RV) observations has facilitated the discovery and characterization of planets orbiting young stars. One such technique, Gaussian process (GP) regression, has been regularly employed to improve the detection of a growing number of planets, but the impact of this model for mitigating stellar activity has not been uniformly analyzed for a large sample with real observations. The goal of this study is to investigate how GPs can affect the inferred parameters of RV-detected planets. We homogeneously analyze how two commonly adopted GP frameworks, a GP trained on RVs alone and a GP pretrained on photometry and then applied to RVs, can influence the inferred physical and orbital parameters compared to a traditional Keplerian orbit fit. Our sample comprises 17 short-period giant planets orbiting stars that exhibit a broad range of activity levels. We find that the decision to adopt GPs, as well as the choice of GP framework, can result in variations of inferred parameters such as minimum planet mass and eccentricity by up to 67% and 95%, respectively. This implies that the method for modeling stellar activity in RVs of young planet-hosting stars can have widespread ramifications on the interpretation of planet properties including their masses, densities, circularization timescales, and tidal quality factors. When mitigating stellar activity with GPs, we recommend carrying out comparative tests between different models to assess the sensitivity of planet physical and orbital parameters to these choices.

Keywords

• Stellar activity
• Radial velocity
• Astronomy data modeling
• Photometry