Track reconstruction with MIMAC

TL;DR

This paper presents a likelihood-based method for reconstructing particle tracks in the MIMAC detector, enhancing directional Dark Matter detection by accurately determining track parameters through detailed simulations.

Contribution

The paper introduces a novel likelihood approach for track reconstruction in MIMAC, improving the accuracy of direction, sense, and position measurements for Dark Matter searches.

Findings

MIMAC can effectively reconstruct particle tracks with high precision.

The likelihood method improves the directional detection capabilities.

Simulations show MIMAC's potential for competitive Dark Matter detection.

Abstract

Directional detection of Dark Matter is a promising search strategy. However, to perform such kind of detection, the recoiling tracks have to be accurately reconstructed: direction, sense and position in the detector volume. In order to optimize the track reconstruction and to fully exploit the data from the MIMAC detector, we developed a likelihood method dedicated to the track reconstruction. This likelihood approach requires a full simulation of track measurements with MIMAC in order to compare real tracks to simulated ones. Finally, we found that the MIMAC detector should have the required performance to perform a competitive directional detection of Dark Matter.