Rejecting noise in Baikal-GVD data with neural networks
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Baikal-GVD is a large (∼ 1 km^3) underwater neutrino telescope installed in the fresh waters of Lake Baikal. The deep lake water environment is pervaded by background light, which produces detectable signals in the Baikal-GVD photosensors. We introduce a neural network for an efficient separation of these noise hits from the signal ones, stemming from the propagation of relativistic particles through the detector. The neural network has a U-net like architecture and employs temporal (causal) structure of events. On Monte-Carlo simulated data, it reaches 99 and 98 data analysis are discussed, and other possible architectures of neural networks, including graph based, are examined.
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