Improving exoplanet detection capabilities with the false inclusion probability. Comparison with other detection criteria in the context of radial velocities

TL;DR

This paper introduces the false inclusion probability (FIP), a new statistical criterion for exoplanet detection in radial velocity data, which improves sensitivity and robustness over existing metrics like FAP and Bayes factor.

Contribution

The paper proposes the FIP as a novel detection metric based on posterior probability, and demonstrates its advantages in sensitivity, reliability, and computational efficiency compared to traditional methods.

Findings

FIP outperforms FAP and Bayes factor in simulations.

FIP effectively tests detection reliability and accounts for aliasing.

Detection sensitivity depends on priors and noise modeling choices.

Abstract

Context. In exoplanet searches with radial velocity data, the most common statistical significance metrics are the Bayes factor and the false alarm probability (FAP). Both have proved useful, but do not directly address whether an exoplanet detection should be claimed. Furthermore, it is unclear which detection threshold should be taken and how robust the detections are to model misspecification. Aims. The present work aims at defining a detection criterion which conveys as precisely as possible the information needed to claim an exoplanet detection. We compare this new criterion to existing ones in terms of sensitivity and robustness. Methods. We define a significance metric called the false inclusion probability (FIP) based on the posterior probability of presence of a planet. Posterior distributions are computed with the nested sampling package Polychord. We show that for FIP and Bayes factor calculations, defining priors on linear parameters as Gaussian mixture models allows to significantly speed up computations. The performances of the FAP, Bayes factor and FIP are studied with simulations as well as analytical arguments. We compare the methods assuming the model is correct, then evaluate their sensitivity to the prior and likelihood choices. Results. Among other properties, the FIP offers ways to test the reliability of the significance levels, it is particularly efficient to account for aliasing and allows to exclude the presence of planets with a certain confidence. We find that, in our simulations, the FIP outperforms existing detection metrics. We show that planet detections are sensitive to priors on period and semi-amplitude and that letting free the noise parameters offers better performances than fixing a noise model based on a fit to ancillary indicators.