Bayesian Methods for Exoplanet Science

TL;DR

This paper introduces Bayesian inference as a powerful statistical framework for analyzing weak and complex signals in exoplanet research, emphasizing its ability to incorporate prior knowledge and heterogeneous data sources.

Contribution

It provides an overview of Bayesian methods tailored for exoplanet time series analysis and reviews available software tools for implementation.

Findings

Bayesian methods effectively disentangle planetary signals from systematics.

Combining multiple observations improves detection reliability.

The paper highlights accessible programming libraries for Bayesian analysis.

Abstract

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.