A background-estimation technique for the detection of extended gamma-ray structures with IACTs

TL;DR

This paper introduces a new background estimation method for IACTs that uses matched observations of emission-free sky regions, enabling analysis of extended gamma-ray structures without needing source-free regions in the field of view.

Contribution

The paper presents a novel run-matching background estimation technique that removes the requirement for emission-free regions, improving analysis of large extended gamma-ray sources.

Findings

Method yields results consistent with standard background models.

Compatible source parameters with previous studies.

Estimates statistical and systematic uncertainties.

Abstract

Estimation of the amount of cosmic-ray induced background events is a challenging task for Imaging Atmospheric Cherenkov Telescopes (IACTs). Most approaches rely on a model of the background signal derived from archival observations, which is then normalised to the region of interest (ROI) and respective observation conditions using emission-free regions in the observation.This is, however, disadvantageous for the analysis of large, extended $\gamma$-ray structures, where no sufficient source free region can be found. We aim to address this issue by estimating the normalisation of a 3-dimensional background model template from separate, matched observations of emission-free sky regions. As a result, the need for a emission-free region in the field of view of the observation becomes unnecessary. For this purpose, we implement an algorithm to identify observation pairs with as close as possible observation conditions. The open-source analysis package Gammapy is utilized for estimating the background rate, facilitating seamless adaptation of the framework to many $\gamma$-ray detection facilities. Public data from the High Energy Stereoscopic System (H.E.S.S.) is employed to validate this methodology. The analysis demonstrates that employing a background rate estimated through this run-matching approach yields results consistent with those obtained using the standard application of the background model template. Furthermore, the compatibility of the source parameters obtained through this approach with previous publications and an analysis employing the background model template approach is confirmed, along with an estimation of the statistical and systematic uncertainties introduced by this method.