Dark Matter Halo Parameters from Overheated Exoplanets via Bayesian Hierarchical Inference

TL;DR

This paper proposes a Bayesian hierarchical model to detect dark matter-induced heating in exoplanets, aiming to infer galactic dark matter halo parameters from observable exoplanet data.

Contribution

It introduces a novel Bayesian framework to analyze exoplanet data for dark matter detection and halo parameter estimation, considering measurement uncertainties and sample sizes.

Findings

Detection of ~100 exoplanets can inform dark matter density profiles.

Even 10 exoplanets can provide meaningful constraints with high dark matter density.

The method is robust under 10% measurement uncertainties.

Abstract

Dark Matter (DM) can become captured, deposit annihilation energy, and hence increase the heat flow in exoplanets and brown dwarfs. Detecting such a DM-induced heating in a population of exoplanets in the inner kpc of the Milky Way thus provides potential sensitivity to the galactic DM halo parameters. We develop a Bayesian Hierarchical Model to investigate the feasibility of DM discovery with exoplanets and examine future prospects to recover the spatial distribution of DM in the Milky Way. We reconstruct from mock exoplanet datasets observable parameters such as exoplanet age, temperature, mass, and location, together with DM halo parameters, for representative choices of measurement uncertainty and the number of exoplanets detected. We find that detection of $\mathcal{O}(100)$ exoplanets in the inner Galaxy can yield quantitative information on the galactic DM density profile, under the assumption of 10% measurement uncertainty. Even as few as $\mathcal{O}(10)$ exoplanets can deliver meaningful sensitivities if the DM density and inner slope are sufficiently large.