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Title: Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies

We present that the minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to γ-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 magnitudeM 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 of simulated and real Fermi-Large Area Telescope (LAT) fields. Our results show that √M is strongly correlated with other statistical significance parameters, derived from a wavelet based algorithm and maximum likelihood (ML) analysis, and that it can be used as a good estimator of statistical significance of MST detections. Finally, we apply the method tomore » a 2-year LAT image at energies higher than 3 GeV, and we show the presence of new clusters, likely associated with BL Lac objects.« less
Authors:
 [1] ;  [2] ;  [3] ;  [3] ;  [4]
  1. INAF/IAPS, Rome and INAF/IASF-Bologna (Italy)
  2. University of Roma Tre, Rome (Italy). INFN/LNF and Department of Physics
  3. University of Rome La Sapienza, Rome (Italy). Department of Physics
  4. University of Perugia, Perugia (Italy). Department of Physics
Publication Date:
Grant/Contract Number:
AC02-76SF00515
Type:
Accepted Manuscript
Journal Name:
Astrophysics and Space Science
Additional Journal Information:
Journal Volume: 347; Journal Issue: 1; Journal ID: ISSN 0004-640X
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
USDOE
Contributing Orgs:
Fermi-LAT Collaboration
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; Gamma rays: general; Methods: data analysis
OSTI Identifier:
1356592

Campana, R., Bernieri, E., Massaro, E., Tinebra, F., and Tosti, G.. Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies. United States: N. p., Web. doi:10.1007/s10509-013-1488-0.
Campana, R., Bernieri, E., Massaro, E., Tinebra, F., & Tosti, G.. Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies. United States. doi:10.1007/s10509-013-1488-0.
Campana, R., Bernieri, E., Massaro, E., Tinebra, F., and Tosti, G.. 2013. "Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies". United States. doi:10.1007/s10509-013-1488-0. https://www.osti.gov/servlets/purl/1356592.
@article{osti_1356592,
title = {Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies},
author = {Campana, R. and Bernieri, E. and Massaro, E. and Tinebra, F. and Tosti, G.},
abstractNote = {We present that the minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to γ-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 magnitudeM 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 of simulated and real Fermi-Large Area Telescope (LAT) fields. Our results show that √M is strongly correlated with other statistical significance parameters, derived from a wavelet based algorithm and maximum likelihood (ML) analysis, and that it can be used as a good estimator of statistical significance of MST detections. Finally, we apply the method to a 2-year LAT image at energies higher than 3 GeV, and we show the presence of new clusters, likely associated with BL Lac objects.},
doi = {10.1007/s10509-013-1488-0},
journal = {Astrophysics and Space Science},
number = 1,
volume = 347,
place = {United States},
year = {2013},
month = {5}
}