Statistical methods for exoplanet detection with radial velocities

TL;DR

This paper reviews statistical methods for detecting exoplanets via radial velocity data, emphasizing challenges in modeling complex signals and exploring parameter spaces to improve detection of Earth-like planets.

Contribution

It unifies existing advanced approaches in radial velocity data analysis, highlighting methodological and numerical challenges for detecting small exoplanets.

Findings

Identifies key challenges in modeling correlated signals.

Highlights the importance of efficient parameter space exploration.

Proposes a unified framework for RV data analysis.

Abstract

Exoplanets can be detected with various observational techniques. Among them, radial velocity (RV) has the key advantages of revealing the architecture of planetary systems and measuring planetary mass and orbital eccentricities. RV observations are poised to play a key role in the detection and characterization of Earth twins. However, the detection of such small planets is not yet possible due to very complex, temporally correlated instrumental and astrophysical stochastic signals. Furthermore, exploring the large parameter space of RV models exhaustively and efficiently presents difficulties. In this review, we frame RV data analysis as a problem of detection and parameter estimation in unevenly sampled, multivariate time series. The objective of this review is two-fold: to introduce the motivation, methodological challenges, and numerical challenges of RV data analysis to nonspecialists, and to unify the existing advanced approaches in order to identify areas for improvement.