Disentangling Stellar Activity and Planetary Signals Using Bayesian High-dimensional Analysis

• Activity Number: 253 - Innovations in AstroStatistics on Exploring Large Public Data
• Type: Invited
• Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Astrostatistics Special Interest Group
• Author(s): Bo Ning* and Jessi Cisewski-Kehe and Allen Davis and Parker Holzer and Debra Fischer
• Companies: Yale University and Yale University and Yale University and Yale University and Yale University
• Keywords: Astrostatistcs; Bayesian variable selection; Bayesian sparse PCA; exoplanets; stellar activity

Abstract

As the development of third-generation high precision spectrometers (e.g., EXtreme PREcision Spectrometer (EXPRES), the stellar activity has become the dominant background noise that can lead to false discoveries or poor mass estimates of small planets. Recent efforts are putting on finding those stellar activity-sensitive lines from a given set of spectra. Since there are ~10^5 features in a typical spectrum, finding those lines can be challenging and time-consuming if using those proposed line-by-line search approaches. In this talk, a Bayesian variable selection method is introduced to automatically search for activity-sensitive lines through pixels from a set of spectra. We applied this method to study the spectra of alpha Centauri B from HARPS. The results are promising. We identified not only many well-known lines that are sensitive to activity, but also several new lines. With stellar activity being the largest source of variability for next-generation RV spectrographs, this work is a step toward accessing the myriad information available in high-precision spectra.