Models of radial velocities and transit light curves

TL;DR

This chapter reviews models for radial velocities and transit light curves in exoplanet research, emphasizing data modeling, stochastic processes, and Bayesian inference to improve planet detection and characterization.

Contribution

It provides a comprehensive derivation of models for radial velocities and transit light curves, including stochastic modeling and Bayesian inference methods, for the first time in a unified framework.

Findings

Derivation of radial velocity and transit models from the two-body problem

Introduction of stochastic models for stellar activity effects

Overview of Bayesian inference techniques for exoplanet data analysis

Abstract

Research in extrasolar-planet science is data-driven. With the advent of radial-velocity instruments like HARPS and HARPS-N, and transit space missions like Kepler, our ability to discover and characterise extrasolar planets is no longer limited by instrumental precision but by our ability to model the data accurately. This chapter presents the models that describe radial-velocity measurements and transit light curves. I begin by deriving the solution of the two-body problem and from there, the equations describing the radial velocity of a planet-host star and the distance between star and planet centres, necessary to model transit light curves. Stochastic models are then presented and I delineate how they are used to model complex physical phenomena affecting the exoplanet data sets, such as stellar activity. Finally, I give a brief overview of the processes of Bayesian inference, focussing on the construction of likelihood functions and prior probability distributions. In particular, I describe different methods to specify ignorance priors.