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An efficient hit finding algorithm for Baikal-GVD muon reconstruction
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The Baikal-GVD is a large scale neutrino telescope being constructed in Lake Baikal. The majority of signal detected by the telescope are noise hits, caused primarily by the luminescence of the Baikal water. Separating noise hits from the hits produced by Cherenkov light emitted from the muon track is a challenging part of the muon event reconstruction. We present an algorithm that utilizes a known directional hit causality criterion to contruct a graph of hits and then use a clique-based technique to select the subset of signal hits.The algorithm was tested on realistic detector Monte-Carlo simulation for a wide range of muon energies and has proved to select a pure sample of PMT hits from Cherenkov photons while retaining above 90\% of original signal.
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Cited by 1 Pith paper
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From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD
A transformer-based three-stage neural network pipeline filters Baikal-GVD data to suppress air showers and noise while selecting neutrino candidates faster and more accurately than standard methods, using domain adap...
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