Exclusion, Discovery and Identification of Dark Matter with Directional Detection

TL;DR

This paper introduces a Bayesian analysis framework for directional dark matter detectors, emphasizing their potential to set exclusion limits, verify detections, and constrain dark matter properties from particle and galactic physics perspectives.

Contribution

It develops a novel Bayesian analysis method tailored for directional detection data, enhancing dark matter search capabilities.

Findings

Demonstrates the framework's effectiveness in setting exclusion limits.

Shows how directional detection can authenticate dark matter signals.

Highlights the potential to constrain dark matter properties.

Abstract

Directional detection is a promising search strategy to discover galactic Dark Matter. We present a Bayesian analysis framework dedicated to data from upcoming directional detectors. 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.