Minimal spanning tree algorithm for gamma-ray source detection in sparse photon images: cluster parameters and selection strategies
R. Campana, E. Bernieri, E. Massaro, F. Tinebra, G. Tosti

TL;DR
This paper enhances the minimal spanning tree algorithm for gamma-ray source detection by introducing a cluster parameter-based filtering method, improving the identification of faint sources in sparse photon images.
Contribution
It introduces a new filtering strategy using the cluster magnitude M to reduce spurious detections and demonstrates its effectiveness with simulated and real Fermi-LAT data.
Findings
Cluster magnitude M correlates with statistical significance measures.
The method detects new likely BL Lac objects in LAT data.
sqrt(M) serves as a reliable estimator of detection significance.
Abstract
The minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to gamma-ray bidimensional images, showing that it is quite sensitive in finding faint sources. Possible sources are associated with the regions where the photon arrival directions clusterize. MST selects clusters starting from a particular "tree" connecting all the point of the image and performing a cut based on the angular distance between photons, with a number of events higher than a given threshold. In this paper, we show how a further filtering, based on some parameters linked to the cluster properties, can be applied to reduce spurious detections. We find that the most efficient parameter for this secondary selection is the magnitude M of a cluster, defined as the product of its number of events by its clustering degree. We test the sensitivity of the method by means…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
