Halo-independence with quantified maximum entropy at DAMA/LIBRA

TL;DR

This paper introduces a Bayesian framework using quantified maximum entropy to analyze DAMA/LIBRA dark matter data, allowing a smooth transition between halo-independent and halo-dependent assumptions and identifying the most plausible dark matter profiles.

Contribution

It formalizes halo-independence within Bayesian statistics using maximum entropy, incorporating an adjustable prior to interpolate between halo assumptions.

Findings

Identifies plausible dark matter profiles consistent with DAMA/LIBRA data.

Provides a method to quantify the impact of prior assumptions on dark matter analysis.

Demonstrates the flexibility of the approach in exploring different halo models.

Abstract

Using the DAMA/LIBRA anomaly as an example, we formalise the notion of halo-independence in the context of Bayesian statistics and quantified maximum entropy. We consider an infinite set of possible profiles, weighted by an entropic prior and constrained by a likelihood describing noisy measurements of modulated moments by DAMA/LIBRA. Assuming an isotropic dark matter (DM) profile in the galactic rest frame, we find the most plausible DM profiles and predictions for unmodulated signal rates at DAMA/LIBRA. The entropic prior contains an a priori unknown regularisation factor, $\beta$, that describes the strength of our conviction that the profile is approximately Maxwellian. By varying $\beta$, we smoothly interpolate between a halo-independent and a halo-dependent analysis, thus exploring the impact of prior information about the DM profile.