Exclusion limits from data of directional Dark Matter detectors

TL;DR

This paper presents a Bayesian statistical method based on extended likelihood to set exclusion limits in directional Dark Matter detection, emphasizing angular data analysis and assessing detector configuration impacts.

Contribution

It introduces an optimal Bayesian approach focusing on angular distributions for exclusion limits, considering detector effects and astrophysical uncertainties.

Findings

The proposed method outperforms existing techniques.

Angular data provides robust exclusion limits.

Detector configuration significantly influences sensitivity.

Abstract

Directional detection is a promising search strategy to discover galactic Dark Matter. Taking advantage on the rotation of the Solar system around the Galactic center through the Dark Matter halo, it allows to show a direction dependence of WIMP events. Even though the goal of directional search is to identify a WIMP positive detection, exclusion limits are still needed for very low exposure with a rather large background contamination, such as the one obtained with prototype experiments. Data of directional detectors are composed of energy and 3D track of recoiling nuclei. However, to set robust exclusion limits, we focus on the angular part of the event distribution, arguing that the energy part of the WIMP distribution is featureless and may even be mimic by the background one. Then, as the angular distributions of both background and WIMP events are known, a Bayesian approach to set exclusion limits is possible. In this paper, a statistical method based on an extended likelihood is proposed, compared to existing ones and is shown to be optimal. Eventually, a comprehensive study of the effect of detector configuration on exclusion limits is presented. It includes the effect of having or not sense recognition, a finite angular resolution, taking into account energy threshold as well as some astrophysical uncertainties.