Paper detail

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

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.