A MST algorithm for source detection in gamma-ray images

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.

10 pages, 7 figures. Accepted for publication in MNRAS

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• Large Scale Structure in the SDSS DR1 Galaxy Survey
• Large Scale Structure in the SDSS Galaxy Survey
• Optimal detection of sources on a homogeneous and isotropic background
• Scale-Adaptive filters for the detection/separation of compact sources

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