Optimum Acceptance Regions for Direct Dark Matter Searches

TL;DR

This paper introduces an algorithm to optimize acceptance regions in dark matter detection experiments, enhancing sensitivity and improving limits on WIMP interactions by tailoring regions to specific models and masses.

Contribution

The paper presents a novel algorithm for defining optimal acceptance regions in direct dark matter searches, improving sensitivity over traditional methods.

Findings

Optimized acceptance regions lead to stronger limits on dark matter interactions.

The algorithm adapts acceptance regions for segmented detectors, allowing for more effective data combination.

Application to CRESST-II data demonstrates improved sensitivity compared to conventional approaches.

Abstract

Most experiments that search for direct interactions of WIMP dark matter with a target can distinguish the dominant electronrecoil background from the nuclear recoil signal, based on some discrimination parameter. An acceptance region is defined inthe parameter space spanned by the recoil energy and this discrimination parameter. In the absence of a clear signal in thisregion, a limit is calculated on the dark matter scattering cross section. Here, an algorithm is presented that allows to define the acceptance region a priori such that the experiment has the best sensitivity. This is achieved through optimized acceptance regions for each WIMP model and WIMP mass that is to be probed. Using recent data from the CRESST-II experiment as anexample, it is shown that resulting limits can be substantially stronger than those from a conventional acceptance region. In an experiment with a segmented target, the algorithm developed here can yield different acceptance regions for the individual subdetectors. Hence, it is shown how to combine the data consistently within the usual Maximum Gap or Optimum Interval framework.