Improved Sensitivity of the DRIFT-IId Directional Dark Matter Experiment using Machine Learning

TL;DR

This paper introduces a machine learning-based analysis for the DRIFT-IId dark matter detector, significantly improving sensitivity at low energies and enabling detection of lighter WIMPs, marking a major advancement in directional dark matter searches.

Contribution

The paper presents a novel machine learning analysis method that enhances the sensitivity of the DRIFT-IId detector, especially at low energies, surpassing previous limits.

Findings

Projected sensitivity improves by an order of magnitude below 15 GeV WIMP mass.

Sensitivity limit reaches down to 9 GeV WIMP mass, a first for directional detectors.

Analysis efficiency at low energies is increased compared to previous methods.

Abstract

We demonstrate a new type of analysis for the DRIFT-IId directional dark matter detector using a machine learning algorithm called a Random Forest Classifier. The analysis labels events as signal or background based on a series of selection parameters, rather than solely applying hard cuts. The analysis efficiency is shown to be comparable to our previous result at high energy but with increased efficiency at lower energies. This leads to a projected sensitivity enhancement of one order of magnitude below a WIMP mass of 15 GeV c$^{-2}$ and a projected sensitivity limit that reaches down to a WIMP mass of 9 GeV c$^{-2}$, which is a first for a directionally sensitive dark matter detector.