A likelihood analysis for gamma-ray background models

TL;DR

This paper compares empirical and theoretical gamma-ray background models using a likelihood approach, highlighting the effectiveness of empirical models in fitting Fermi-LAT data for dark matter searches.

Contribution

It introduces a likelihood-based framework to evaluate and compare empirical and theoretical gamma-ray background models, including new covariance-based empirical methods.

Findings

Empirical models fit gamma-ray data as well as theoretical models.

Covariance-based empirical models effectively capture cross-energy correlations.

Empirical models are statistically competitive in high-latitude regions.

Abstract

Indirect searches for dark matter using dwarf spheroidal galaxies are limited by systematic uncertainties in modeling diffuse gamma-ray backgrounds. We present a likelihood-based comparison of locally constructed empirical background models and theoretically-motivated models that incorporate the Fermi-LAT diffuse background. The empirical models we study include both an independent-binning approach and a covariance-based approach that captures cross-energy correlations. Using ensembles of blank-sky regions and information criteria which account for model complexity, we find that empirical background descriptions provide a statistically competitive fit to gamma-ray data on degree scales in high-latitude regions.