Quantifying (dis)agreement between direct detection experiments in a halo-independent way

TL;DR

This paper introduces a halo-independent, likelihood-based method to analyze small-sample dark matter detection data, assessing consistency among experiments and identifying best-fit parameters.

Contribution

It develops a new global analysis framework that is truly halo-independent and suitable for small event numbers, improving the assessment of dark matter signals across experiments.

Findings

Method effectively tests consistency between experiments.

Likelihood function enables simultaneous parameter estimation.

Monte Carlo simulations validate the statistical approach.

Abstract

We propose an improved method to study recent and near-future dark matter direct detection experiments with small numbers of observed events. Our method determines in a quantitative and halo-independent way whether the experiments point towards a consistent dark matter signal and identifies the best-fit dark matter parameters. To achieve true halo independence, we apply a recently developed method based on finding the velocity distribution that best describes a given set of data. For a quantitative global analysis we construct a likelihood function suitable for small numbers of events, which allows us to determine the best-fit particle physics properties of dark matter considering all experiments simultaneously. Based on this likelihood function we propose a new test statistic that quantifies how well the proposed model fits the data and how large the tension between different direct detection experiments is. We perform Monte Carlo simulations in order to determine the probability distribution function of this test statistic and to calculate the p-value for both the dark matter hypothesis and the background-only hypothesis.