Directional Detection of Dark Matter with MIMAC

TL;DR

This paper presents a Bayesian analysis framework for directional dark matter detection, emphasizing the importance of accurate track reconstruction with the MIMAC detector to improve detection sensitivity and constrain dark matter properties.

Contribution

It introduces a new Bayesian analysis method tailored for directional detection data, enhancing dark matter search strategies and detector data interpretation.

Findings

Directional detection can effectively set exclusion limits on dark matter.

Bayesian analysis improves the interpretation of directional detection data.

Accurate track reconstruction is crucial for reliable dark matter detection.

Abstract

Directional detection is a promising search strategy to discover galactic Dark Matter. We present a Bayesian analysis framework dedicated to Dark Matter phenomenology using directional detection. The interest of directional detection as a powerful tool to set exclusion limits, to authentify a Dark Matter detection or to constrain the Dark Matter properties, both from particle physics and galactic halo physics, will be demonstrated. However, such results need highly accurate track reconstruction which should be reachable by the MIMAC detector using a dedicated readout combined with a likelihood analysis of recoiling nuclei.