A MST algorithm for source detection in gamma-ray images

TL;DR

This paper presents a novel source detection algorithm for gamma-ray images using the Minimal Spanning Tree (MST) method, effectively identifying clusters of photon arrival directions to detect astrophysical sources.

Contribution

The paper introduces a new MST-based algorithm with filters and parameters for reliable source detection in gamma-ray images, including empirical statistical analysis and practical application.

Findings

Successfully detected multiple sources in EGRET Virgo field data.

Developed criteria and parameters to distinguish real sources from spurious detections.

Provided statistical analysis of MST properties on random fields.

Abstract

We developed a source detection algorithm based on the Minimal Spanning Tree (MST), that is a graph-theoretical method useful for finding clusters in a given set of points. This algorithm is applied to gamma-ray bidimensional images where the points correspond to the arrival direction of photons, and the possible sources are associated with the regions where they clusterize. Some filters to select these clusters and to reduce the spurious detections are introduced. An empirical study of the statistical properties of MST on random fields is carried in order to derive some criteria to estimate the best filter values. We introduce also two parameters useful to verify the goodness of candidate sources. To show how the MST algorithm works in the practice, we present an application to an EGRET observation of the Virgo field, at high galactic latitude and with a low and rather uniform background, in which several sources are detected.