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      Model-independent search for neutrino sources with the ANTARES neutrino telescope

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          Abstract

          A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5\(\sigma\). Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1\(\sigma\).

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          The random subspace method for constructing decision forests

          Tin Ho (1998)
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            Author and article information

            Journal
            2017-03-13
            Article
            1703.04351
            eb0a1e39-71b9-41ed-b7ac-ecbd6a025bf5

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

            History
            Custom metadata
            85-04, 85-05, 85-08
            astro-ph.HE

            High energy astrophysical phenomena
            High energy astrophysical phenomena

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