Fishing for Planets: A Comparative Analysis of EPRV Survey Performance in the Presence of Correlated Noise

TL;DR

This paper analyzes the effectiveness of different observational strategies in exoplanet surveys using radial velocity methods, considering correlated stellar noise, to optimize detection of Earth-like planets.

Contribution

It introduces a Fisher information-based framework incorporating Gaussian Process models of stellar variability to evaluate and compare survey scheduling strategies.

Findings

Optimal observation scheduling improves Earth-like planet detection.

Correlated stellar noise significantly impacts survey sensitivity.

Recommendations for survey design to enhance low-amplitude signal detection.

Abstract

With dedicated exoplanet surveys underway for multiple extreme precision radial velocity (EPRV) instruments, the near-future prospects of RV exoplanet science are promising. These surveys' generous time allocations are expected to facilitate the discovery of Earth analogs around bright, nearby Sun-like stars. But survey success will depend critically on the choice of observing strategy, which will determine the survey's ability to mitigate known sources of noise and extract low-amplitude exoplanet signals. Here, we present an analysis of the Fisher information content of simulated EPRV surveys, accounting for the most recent advances in our understanding of stellar variability on both short and long timescales (i.e., oscillations and granulation within individual nights, and activity-induced variations across multiple nights). In this analysis, we capture the correlated nature of stellar variability by parameterizing these signals with Gaussian Process kernels. We describe the underlying simulation framework as well as the physical interpretation of the Fisher information content, and we evaluate the efficacy of EPRV survey strategies that have been presented in the literature. We explore and compare strategies for scheduling observations over various timescales and we make recommendations to optimize survey performance for the detection of Earth-like exoplanets.