Vector spaces for dark matter (VSDM): Fast direct detection calculations with Python and Julia

Key findings from this study

• The authors developed VSDM implementations in Julia and Python to accelerate dark matter scattering rate computations for anisotropic materials.
• The framework pre-computes partial rate matrices spanning dark matter velocity distributions, material response functions, and dark matter particle properties.
• The study demonstrates that pre-computation strategies enable rapid evaluation across experimental parameter spaces without redundant calculations.

Overview

The paper introduces Julia and Python implementations of Vector Spaces for Dark Matter (VSDM), a computational framework designed to calculate scattering rates in anisotropic target materials used for dark matter direct detection. Anisotropic materials offer directional sensitivity to distinguish dark matter signals from Standard Model backgrounds. VSDM handles complex three-dimensional rotating response functions by pre-computing partial rate matrices across combinations of dark matter velocity distributions, material response functions, and particle dark matter properties.

Methods and approach

VSDM generates partial rate matrices that span the parameter space of dark matter direct detection calculations. The framework systematically combines dark matter velocity distributions with material-specific response tensors and dark matter particle properties. Dual implementations in Julia and Python enable computational efficiency and accessibility to different research communities.

Results

The authors developed computational tools that substantially accelerate scattering rate calculations for anisotropic dark matter targets. The pre-computation strategy enables rapid evaluation across multiple parameter combinations without recalculating fundamental physical quantities. Implementation in both Julia and Python provides flexibility for integration into different experimental analysis pipelines.

Implications

Fast scattering rate calculations remove computational barriers to analyzing directional dark matter detection experiments. Anisotropic target materials represent a promising avenue for dark matter discovery, and accessible computational tools lower technical obstacles to their widespread adoption. These implementations facilitate comparative studies across different target materials and dark matter hypotheses within the same computational framework.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.