Identifying Activity-sensitive Spectral Lines: A Bayesian Variable Selection Approach

Stellar activity, such as spots and faculae, provides a noise background that may lead to false discoveries or poor mass estimates of small planets when using radial velocity (RV) techniques. Spectroscopic activity indices are often used to verify the authenticity of planet candidates. Recently, Wise et al. proposed a method to identify activity-sensitive lines through finding lines that are significantly correlated with the S-index. Their study is novel but has three limitations: their method requires the manual selection of a set of lines before conducting an analysis, dependencies between lines are ignored when calculating correlations, and using the S-index is not sufficient for identifying all activity-sensitive lines, as S-index only captures some manifestations of stellar activity. In this paper, we develop a Bayesian variable selection method that can address these limitations. Our method can automatically search for activity-sensitive lines through pixels from a set of spectra. We not only use the S-index, but also include the H? and NaD indices, the bisector inverse slope, and the full width at half maximum. The details of the activity-sensitive lines are listed in the paper. Machine-readable tables and the code of the statistical method are available online. 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.