Dwarf spheroidal J-factors without priors: A likelihood-based analysis for indirect dark matter searches

TL;DR

This paper introduces a new frequentist method to estimate J-factors in dwarf spheroidal galaxies for dark matter searches, reducing prior-related uncertainties and providing reliable confidence intervals based on stellar kinematic data.

Contribution

A novel likelihood-based approach for J-factor estimation that avoids priors, validated with simulations, and applicable to dark matter indirect detection.

Findings

The method produces unbiased J-factor estimates with good coverage.

Results are consistent with Bayesian methods for larger datasets.

Largest deviations occur with very small kinematic samples.

Abstract

Line-of-sight integrals of the squared density, commonly called the J-factor, are essential for inferring dark matter annihilation signals. The J-factors of dark matter-dominated dwarf spheroidal satellite galaxies (dSphs) have typically been derived using Bayesian techniques, which for small data samples implies that a choice of priors constitutes a non-negligible systematic uncertainty. Here we report the development of a new fully frequentist approach to construct the profile likelihood of the J-factor. Using stellar kinematic data from several classical and ultra-faint dSphs, we derive the maximum likelihood value for the J-factor and its confidence intervals. We validate this method, in particular its bias and coverage, using simulated data from the Gaia Challenge. We find that the method possesses good statistical properties. The J-factors and their uncertainties are generally in good agreement with the Bayesian-derived values, with the largest deviations restricted to the systems with the smallest kinematic datasets. We discuss improvements, extensions, and future applications of this technique.