Abstract

The radial velocity method is one of the most successful techniques for detecting exoplanets. It works by detecting the velocity of a host star induced by the gravitational effect of an orbiting planet, specifically the velocity along our line of sight, which is called the radial velocity of the star. As astronomical instrumentation has improved, radial velocity surveys have become sensitive to low-mass planets that cause their host star to move with radial velocities of 1 m/s or less. While analysis of a time series of stellar spectra can in theory reveal such small radial velocities, in practice intrinsic stellar variability (e.g., star spots, convective motion, pulsations) affects the spectra and often mimics a radial velocity signal. This signal contamination makes it difficult to reliably detect low mass planets and planets orbiting magnetically active stars. A principled approach to recovering planet radial velocity signals in the presence of stellar activity was proposed by Rajpaul et al. (2015) and involves the use of a multivariate Gaussian process model to jointly capture time series of the apparent radial velocity and multiple indicators of stellar activity. We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity indicators that make use of a larger portion of the stellar spectrum; (ii) we extend the Rajpaul et al. (2015) model to a larger class of models and use a model comparison procedure to select the best model for the particular stellar activity indicators at hand. By combining our high-information stellar activity indicators, Gaussian process models, and model selection procedure, we achieve substantially improved planet detection power compared to previous state-of-the-art approaches.

Keywords Signal detection, astrostatistics, Gaussian process modeling, dimension reduction, principal component analysis.