Surrogate Models for Direct Dark Matter Detection

TL;DR

This paper introduces RAPIDD, a fast surrogate model for calculating expected dark matter detection spectra, enabling efficient exploration of complex parameter spaces in direct detection experiments.

Contribution

The paper presents RAPIDD, a novel polynomial-based surrogate model that accelerates the computation of dark matter spectra, validated across multi-dimensional parameter spaces including astrophysical uncertainties.

Findings

RAPIDD is significantly faster than exact calculations.

The surrogate model maintains high accuracy across parameter space.

It effectively facilitates multi-dimensional dark matter analysis.

Abstract

In this work we introduce RAPIDD, a surrogate model that speeds up the computation of the expected spectrum of dark matter particles in direct detection experiments. RAPIDD replaces the exact calculation of the dark matter differential rate (which in general involves up to three nested integrals) with a much faster parametrization in terms of ordinary polynomials of the dark matter mass and couplings, obtained in an initial training phase. In this article, we validate our surrogate model on the multi-dimensional parameter space resulting from the effective field theory description of dark matter interactions with nuclei, including also astrophysical uncertainties in the description of the dark matter halo. As a concrete example, we use this tool to study the complementarity of different targets to discriminate simplified dark matter models. We demonstrate that RAPIDD is fast and accurate, and particularly well-suited to explore a multi-dimensional parameter space, such as the one in effective field theory approach, and scans with a large number of evaluations.