Morphometric analysis in gamma-ray astronomy using Minkowski functionals: III. Sensitivity increase via a refined structure quantification

TL;DR

This paper introduces a refined morphometric method using Minkowski functionals to improve the detection sensitivity of gamma-ray sources by analyzing structural deviations in counts maps without prior source knowledge.

Contribution

It presents a novel application of joint Minkowski functional analysis for enhanced source detection sensitivity in gamma-ray astronomy.

Findings

Joint Minkowski functional analysis detects previously unseen sources.

Additional shape information increases detection sensitivity.

Method surpasses standard counting approaches in sensitivity.

Abstract

We pursue a novel morphometric analysis to detect sources in very-high-energy gamma-ray counts maps by structural deviations from the background noise without assuming any prior knowledge about potential sources. The rich and complex structure of the background noise is characterized by Minkowski functionals from integral geometry. By extracting more information out of the same data, we aim for an increased sensitivity. In the first two papers, we derived accurate estimates of the joint distribution of all Minkowski functionals. Here, we use this detailed structure characterization to detect structural deviations from the background noise in a null hypothesis test. We compare the analysis of the same simulated data with either a single or all Minkowski functionals. The joint structure quantification can detect formerly undetected sources. We show how the additional shape information leads to the increase in sensitivity. We explain the very unique concepts and possibilites of our analysis compared to a standard counting method in gamma-ray astronomy, and we present in an outlook further improvements especially for the detection of diffuse background radiation and generalizations of our technique.