A novel framework for semi-Bayesian radial velocities through template matching

Abstract

The detection and characterization of an increasing variety of exoplanets has been in part possible thanks to the continuous development of high-resolution, stable spectrographs, and using the Doppler radial-velocity (RV) method. The Cross Correlation Function (CCF) method is one of the traditional approaches for RV extraction. More recently, template matching was introduced as an advantageous alternative for M-dwarf stars. In this paper, we describe a new implementation of template matching within a semi-Bayesian framework, providing a more statistically principled characterization of the RV measurements. In this context, a common RV shift is used to describe the difference between each spectral order of a given stellar spectrum and a template built from the available observations. Posterior probability distributions are obtained for the relative RV associated with each spectrum, after marginalizing with respect to the continuum. This methodology was named S-BART: Semi-Bayesian Approach for RVs with Template-matching, and it can be applied to HARPS and ESPRESSO. The application of our method to HARPS archival observations of Barnard's star allowed us to validate our implementation against HARPS-TERRA and SERVAL. Then, we applied it to 33 ESPRESSO targets, evaluating its performance and comparing it with the CCF method. We found a decrease in the median RV scatter of \sim 10\% and \sim 4\% for M- and K-type stars, respectively. S-BART yields more precise RV estimates than the CCF method, particularly in the case of M-type stars where a median uncertainty of \sim 15 cm/s is achieved over 309 observations. Further, we estimated the nightly zero point (NZP) of ESPRESSO, finding a weighted NZP scatter below \sim 0.7 m/s. As this includes stellar variability, photon noise, and potential planetary signals, it should be taken as an upper limit of the RV precision attainable with ESPRESSO data.

17 pages, 15 Figures, Accepted for publication by Astronomy & Astrophysics (A&A), Code available in https://github.com/iastro-pt/sBART

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