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- W4386829265 abstract "Abstract 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 is detectable by Baikal-GVD's photosensors. We introduce a neural network for an efficient separation of these noise hits from the signal ones, stemmng from the propagation of relativistic particles through the detector. The model has a U-Net-like architecture and employs temporal (causal) structure of events. The neural network's metrics reach up to 99% signal purity (precision) and 96% survival efficiency (recall) on Monte-Carlo simulated dataset. We compare the developed method with the algorithmic approach to rejecting the noise and discuss other possible architectures of neural networks, including graph-based ones." @default.
- W4386829265 created "2023-09-19" @default.
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- W4386829265 date "2023-09-01" @default.
- W4386829265 modified "2023-09-27" @default.
- W4386829265 title "Rejecting noise in Baikal-GVD data with neural networks" @default.
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- W4386829265 doi "https://doi.org/10.1088/1748-0221/18/09/p09026" @default.
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