Directional detection as a strategy to discover Galactic Dark Matter

TL;DR

This paper introduces a formalism using a map-based likelihood method to analyze directional detection data, enabling the identification of galactic dark matter signals and distinguishing them from background noise.

Contribution

It presents a comprehensive, blind analysis method for directional dark matter detection that can recover signal direction and significance, improving detection confidence.

Findings

The method can identify WIMP signals with high significance.

It allows constraints in the WIMP-nucleon cross section and mass parameter space.

Unambiguous dark matter detection is achievable across various experimental conditions.

Abstract

Directional detection of Galactic Dark Matter is a promising search strategy for discriminating genuine WIMP events from background ones. Technical progress on gaseous detectors and read-outs has permitted the design and construction of competitive experiments. However, to take full advantage of this powerful detection method, one need to be able to extract information from an observed recoil map to identify a WIMP signal. We present a comprehensive formalism, using a map-based likelihood method allowing to recover the main incoming direction of the signal and its significance, thus proving its galactic origin. This is a blind analysis intended to be used on any directional data. Constraints are deduced in the ($\sigma_n, m_\chi$) plane and systematic studies are presented in order to show that, using this analysis tool, unambiguous dark matter detection can be achieved on a large range of exposures and background levels.