gamma-ray DBSCAN: a clustering algorithm applied to Fermi-LAT gamma-ray data. I. Detection performances with real and simulated data

Abstract

The Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a topometric algorithm used to cluster spatial data that are affected by background noise. For the first time, we propose the use of this method for the detection of sources in gamma-ray astrophysical images obtained from the Fermi-LAT data, where each point corresponds to the arrival direction of a photon. We investigate the detection performance of the gamma-ray DBSCAN in terms of detection efficiency and rejection of spurious clusters, using a parametric approach, and exploring a large volume of the gamma-ray DBSCAN parameter space. By means of simulated data we statistically characterize the gamma-ray DBSCAN, finding signatures that differentiate purely random fields, from fields with sources. We define a significance level for the detected clusters, and we successfully test this significance with our simulated data. We apply the method to real data, and we find an excellent agreement with the results obtained with simulated data. We find that the gamma-ray DBSCAN can be successfully used in the detection of clusters in gamma-ray data. The significance returned by our algorithm is strongly correlated with that provided by the Maximum Likelihood analysis with standard Fermi-LAT software, and can be used to safely remove spurious clusters. The positional accuracy of the reconstructed cluster centroid compares to that returned by standard Maximum Likelihood analysis, allowing to look for astrophysical counterparts in narrow regions, minimizing the chance probability in the counterpart association. We find that gamma-ray DBSCAN is a powerful tool in the detection of clusters in gamma-ray data, this method can be used both to look for point-like sources, and extended sources, and can be potentially applied to any astrophysical field related with detection of clusters in data.

Accepted for publication in A&A

References in corpus (3)

• Fermi Large Area Telescope Second Source Catalog
• The Fermi Large Area Telescope On Orbit: Event Classification, Instrument Response Functions, and Calibration
• A MST algorithm for source detection in gamma-ray images

Cited by in corpus (15)

• A census of $\rho$ Oph candidate members from Gaia DR2
• Deciphering the large-scale environment of radio galaxies in the local Universe: where do they born, grow and die?
• The PCA Lens-Finder: application to CFHTLS
• ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification
• Application of the MST clustering to the high energy gamma-ray sky. I - New possible detection of high-energy gamma-ray emission associated with BL Lac objects
• Minimal spanning tree algorithm for gamma-ray source detection in sparse photon images: cluster parameters and selection strategies
• A new flaring high energy gamma-ray source
• A MST catalogue of gamma-ray source candidates above 10 GeV and at Galactic latitudes higher than 20 degrees
• Graph-based clustering of gamma-ray bursts
• An Optical Analysis of the Merging Cluster Abell 3888
• The Detection of Fermi AGN above 100 GeV using Clustering Analysis
• High energy gamma-ray detection of supernova remnants in the Large Magellanic Cloud
• Search for $\gamma$-ray emission from SNRs in the Large Magellanic Cloud: a new cluster analysis at energies above 4 GeV
• Detection of new very-high-energy sources outside the galactic plane in the Fermi-LAT data
• A blind search for intraday gamma-ray transients with Fermi-LAT: Detections of GRB and solar emissions