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Context. In exoplanet searches with radial velocity data, the most commonstatistical significance metrics are the Bayes factor and the false alarmprobability (FAP). Both have proved useful, but do not directly address whetheran exoplanet detection should be claimed. Furthermore, it is unclear whichdetection threshold should be taken and how robust the detections are to modelmisspecification. Aims. The present work aims at defining a detection criterionwhich conveys as precisely as possible the information needed to claim anexoplanet detection. We compare this new criterion to existing ones in terms ofsensitivity and robustness. Methods. We define a significance metric called thefalse inclusion probability (FIP) based on the posterior probability ofpresence of a planet. Posterior distributions are computed with the nestedsampling package Polychord. We show that for FIP and Bayes factor calculations,defining priors on linear parameters as Gaussian mixture models allows tosignificantly speed up computations. The performances of the FAP, Bayes factorand FIP are studied with simulations as well as analytical arguments. Wecompare the methods assuming the model is correct, then evaluate theirsensitivity to the prior and likelihood choices. Results. Among otherproperties, the FIP offers ways to test the reliability of the significancelevels, it is particularly efficient to account for aliasing and allows toexclude the presence of planets with a certain confidence. We find that, in oursimulations, the FIP outperforms existing detection metrics. We show thatplanet detections are sensitive to priors on period and semi-amplitude and thatletting free the noise parameters offers better performances than fixing anoise model based on a fit to ancillary indicators.

astronomy and astrophysics

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