Three-dimensional track reconstruction for directional Dark Matter detection

TL;DR

This paper introduces a likelihood-based 3D track reconstruction method for directional Dark Matter detection, achieving sub-millimeter spatial resolution and moderate angular resolution, enhancing detector capabilities for Dark Matter searches.

Contribution

It presents a novel likelihood method for 3D track reconstruction in directional detectors, enabling improved spatial resolution and analysis of recoil directions for Dark Matter detection.

Findings

Achieves sub-mm spatial resolution in the anode plane

Angular resolution ranges from 20° to 80° depending on recoil energy

Sense recognition efficiency is limited, suggesting focus on axial data below 100 keV

Abstract

Directional detection of Dark Matter is a promising search strategy. However, to perform such detection, a given set of parameters has to be retrieved from the recoiling tracks : direction, sense and position in the detector volume. In order to optimize the track reconstruction and to fully exploit the data of forthcoming directional detectors, we present a likelihood method dedicated to 3D track reconstruction. This new analysis method is applied to the MIMAC detector. It requires a full simulation of track measurements in order to compare real tracks to simulated ones. We conclude that a good spatial resolution can be achieved, i.e. sub-mm in the anode plane and cm along the drift axis. This opens the possibility to perform a fiducialization of directional detectors. The angular resolution is shown to range between 20$^\circ$ to 80$^\circ$, depending on the recoil energy, which is however enough to achieve a high significance discovery of Dark Matter. On the contrary, we show that sense recognition capability of directional detectors depends strongly on the recoil energy and the drift distance, with small efficiency values (50%-70%). We suggest not to consider this information either for exclusion or discovery of Dark Matter for recoils below 100 keV and then to focus on axial directional data.