A continuous multiple hypothesis testing framework for optimal exoplanet detection

TL;DR

This paper introduces a Bayesian framework for optimal exoplanet detection that minimizes false and missed detections, adaptable to continuous hypotheses and robust to model errors, demonstrated through simulations.

Contribution

It develops a continuous multiple hypothesis testing framework for exoplanet detection, extending Bayesian methods to handle non-discrete hypotheses and model uncertainties.

Findings

Optimal detection criterion outperforms existing methods in simulations.

Framework effectively controls false discovery rate in continuous hypothesis settings.

Method applicable to mixture models and component identification.

Abstract

When searching for exoplanets, one wants to count how many planets orbit a given star, and to determine what their characteristics are. If the estimated planet characteristics are too far from those of a planet truly present, this should be considered as a false detection. This setting is a particular instance of a general one: aiming to retrieve parametric components in a dataset corrupted by nuisance signals, with a certain accuracy on their parameters. We exhibit a detection criterion minimizing false and missed detections, either as a function of their relative cost or when the expected number of false detections is bounded. If the components can be separated in a technical sense discussed in detail, the optimal detection criterion is a posterior probability obtained as a by-product of Bayesian evidence calculations. Optimality is guaranteed within a model, and we introduce model criticism methods to ensure that the criterion is robust to model errors. We show on two simulations emulating exoplanet searches that the optimal criterion can significantly outperform other criteria. Finally, we show that our framework offers solutions for the identification of components of mixture models and Bayesian false discovery rate control when hypotheses are not discrete.