Low energy electron/recoil discrimination for directional Dark Matter detection

TL;DR

This paper presents a multivariate analysis method using Boosted Decision Trees to improve electron/recoil discrimination in directional Dark Matter detection, especially at low energies, enhancing background rejection while maintaining efficiency.

Contribution

It introduces a novel multivariate analysis approach for low-energy electron/recoil discrimination in directional Dark Matter detectors, improving rejection power over traditional sequential methods.

Findings

Rejection power is about 20 times higher with multivariate analysis at the same exclusion limit.

The method maintains high efficiency for rare event detection.

Effective discrimination is achieved even at low energies around 20 keV.

Abstract

Directional detection is a promising Dark Matter search strategy. Even though it could accommodate to a sizeable background contamination, electron/recoil discrimination remains a key and challenging issue as for direction-insensitive detectors. The measurement of the 3D track may be used to discriminate electrons from nuclear recoils. While a high rejection power is expected above 20 keV ionization, a dedicated data analysis is needed at low energy. After identifying discriminant observables, a multivariate analysis, namely a Boosted Decision Tree, is proposed, enabling an efficient event tagging for Dark Matter search. We show that it allows us to optimize rejection while keeping a rather high efficiency which is compulsory for rare event search.With respect to a sequential analysis, the rejection is about 20 times higher with a multivariate analysis, for the same Dark Matter exclusion limit.