Locating $$\gamma$$ γ -ray sources on the celestial sphere via modal clustering

Anna Montin (), Alessandra R. Brazzale (), Giovanna Menardi () and Andrea Sottosanti ()
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Anna Montin: University of Padova
Alessandra R. Brazzale: University of Padova
Giovanna Menardi: University of Padova
Andrea Sottosanti: University of Padova

Statistical Methods & Applications, 2024, vol. 33, issue 1, No 6, 153-172

Abstract: Abstract Sky surveys represent the fundamental data basis for detecting and locating as yet undiscovered celestial objects. Since 2008, the Fermi LAT Collaboration has catalogued thousands of $$\gamma$$ γ -ray sources with the aim of extending our knowledge of the highly energetic physical mechanisms and processes that lie at the core of our Universe. In this article, we present a nonparametric clustering algorithm which identifies high-energy astronomical sources using the spatial information of the $$\gamma$$ γ -ray photons detected by the large area telescope onboard the Fermi spacecraft. In particular, the sources are identified using a von Mises–Fisher kernel estimate of the photon count density on the unit sphere via an adjustment of the mean-shift algorithm which accounts for the directional nature of the collected data and the need of local smoothing. This choice entails a number of desirable benefits. It allows us to bypass the difficulties inherent on the borders of any projection of the photon directions onto a 2-dimensional plane, while guaranteeing high flexibility. The smoothing parameter is chosen adaptively, by combining scientific input with optimal selection guidelines, as known from the literature. Using statistical tools from hypothesis testing and classification, we furthermore present an automatic way to skim off sound candidate sources from the $$\gamma$$ γ -ray emitting diffuse background and to quantify their significance. We calibrate and test our algorithm on simulated count maps provided by the Fermi LAT Collaboration.

Keywords: Directional data; Kernel density estimator; Man-shift algorithm; Tree-based classification (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-023-00726-w

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