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

Fuente: arXiv
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Auteurs principaux: Lillard, Benjamin, Radick, Aria
Format: Preprint
Publié: 2025
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author Lillard, Benjamin
Radick, Aria
author_facet Lillard, Benjamin
Radick, Aria
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2502_17547
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Vector Spaces for Dark Matter (VSDM): Fast Direct Detection Calculations with Python and Julia
Lillard, Benjamin
Radick, Aria
High Energy Physics - Phenomenology
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.
title Vector Spaces for Dark Matter (VSDM): Fast Direct Detection Calculations with Python and Julia
topic High Energy Physics - Phenomenology
url https://arxiv.org/abs/2502.17547