Department of Statistics Graduate Student Seminar: David Jones, Postdoctoral Fellow, David Jones, Duke University and SAMSI

Abstract:

The radial velocity method is one of the most successful techniques for detecting exo- planets. However, radial velocity signals are often corrupted by stellar activity making it difficult to detect low mass planets and planets orbiting more active stars. A prin- cipled 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 dependent Gaussian processes to jointly model the corrupted radial velocity signal and multiple proxies for stellar activity. We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity proxies; (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 proxies at hand. For SOAP 2.0 data, our approach results in substantially improved statistical power for planet detection than using existing models from the literature.