Vector Spaces for Dark Matter (VSDM): Fast Direct Detection Calculations with Python and Julia

TL;DR

This paper introduces VSDM, a computational framework in Python and Julia for efficient calculation of dark matter detection rates using anisotropic target materials, enabling better signal-background discrimination.

Contribution

The paper presents novel Python and Julia implementations of VSDM that efficiently compute scattering rates for anisotropic dark matter detection materials.

Findings

Efficient computation of partial rate matrices for various DM velocities and material responses.

Facilitates improved analysis of directional dark matter detection experiments.

Provides accessible tools for the dark matter research community.

Abstract

Anisotropic target materials are promising candidates for dark matter direct detection experiments, providing a directional sensitivity that can be used to distinguish a dark matter (DM) signal from the various Standard Model backgrounds. In this paper we introduce the Julia and Python implementations of \emph{Vector Spaces for Dark Matter} (VSDM), which handle the difficult scattering rate computation for these rotating, three-dimensional response functions by calculating a partial rate matrix for every combination of DM velocity distribution, material response function, and particle DM properties.