Minimal spanning tree algorithm for γray source detection in sparse photon images: cluster parameters and selection strategies
We present that the minimal spanning tree (MST) algorithm is a graphtheoretical clusterfinding method. We previously applied it to γray 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 FermiLarge 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 »
 Authors:

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 INAF/IAPS, Rome and INAF/IASFBologna (Italy)
 University of Roma Tre, Rome (Italy). INFN/LNF and Department of Physics
 University of Rome La Sapienza, Rome (Italy). Department of Physics
 University of Perugia, Perugia (Italy). Department of Physics
 Publication Date:
 Grant/Contract Number:
 AC0276SF00515
 Type:
 Accepted Manuscript
 Journal Name:
 Astrophysics and Space Science
 Additional Journal Information:
 Journal Volume: 347; Journal Issue: 1; Journal ID: ISSN 0004640X
 Research Org:
 SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Sponsoring Org:
 USDOE
 Contributing Orgs:
 FermiLAT 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 γray source detection in sparse photon images: cluster parameters and selection strategies. United States: N. p.,
Web. doi:10.1007/s1050901314880.
Campana, R., Bernieri, E., Massaro, E., Tinebra, F., & Tosti, G.. Minimal spanning tree algorithm for γray source detection in sparse photon images: cluster parameters and selection strategies. United States. doi:10.1007/s1050901314880.
Campana, R., Bernieri, E., Massaro, E., Tinebra, F., and Tosti, G.. 2013.
"Minimal spanning tree algorithm for γray source detection in sparse photon images: cluster parameters and selection strategies". United States.
doi:10.1007/s1050901314880. https://www.osti.gov/servlets/purl/1356592.
@article{osti_1356592,
title = {Minimal spanning tree algorithm for γray 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 graphtheoretical clusterfinding method. We previously applied it to γray 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 FermiLarge 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 2year 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/s1050901314880},
journal = {Astrophysics and Space Science},
number = 1,
volume = 347,
place = {United States},
year = {2013},
month = {5}
}