Dark Matter Model or Mass, but Not Both: Assessing Near-Future Direct Searches with Benchmark-free Forecasting

TL;DR

This paper introduces benchmark-free methods for forecasting the discrimination power of future dark matter direct detection experiments, enabling comprehensive exploration of model distinguishability and parameter constraints.

Contribution

It presents novel benchmark-free forecasting techniques for dark matter searches, allowing detailed analysis of model discrimination and parameter estimation without relying on arbitrary benchmarks.

Findings

Including Argon targets improves detection prospects.

Discrimination of DM properties is feasible in a specific mass and cross-section region.

Most parameter regions only allow either interaction or mass discrimination, not both.

Abstract

Forecasting the signal discrimination power of dark matter (DM) searches is commonly limited to a set of arbitrary benchmark points. We introduce new methods for benchmark-free forecasting that instead allow an exhaustive exploration and visualization of the phenomenological distinctiveness of DM models, based on standard hypothesis testing. Using this method, we reassess the signal discrimination power of future liquid Xenon and Argon direct DM searches. We quantify the parameter regions where various non-relativistic effective operators, millicharged DM, and magnetic dipole DM can be discriminated, and where upper limits on the DM mass can be found. We find that including an Argon target substantially improves the prospects for reconstructing the DM properties. We also show that only in a small region with DM masses in the range 20-100 GeV and DM-nucleon cross sections a factor of a few below current bounds can near-future Xenon and Argon detectors discriminate both the DM-nucleon interaction and the DM mass simultaneously. In all other regions only one or the other can be obtained.