• Corpus ID: 238253318

Machine learning aided noise filtration and signalc lassification for CREDO experiment

@inproceedings{Bibrzycki2021MachineLA,
  title={Machine learning aided noise filtration and signalc lassification for CREDO experiment},
  author={Lukasz Bibrzycki and David E. Alvarez-Castillo and Olaf Bar and Dariusz G{\'o}ra and Piotr Homola and P'eter Kov'acs and Michal Nied'zwiecki and Marcin Piekarczyk and Krzysztof Rzecki and Jaroslaw Stasielak and Sławomir Stuglik and Oleksandr Sushchov and Arman Tursunov},
  year={2021}
}
The wealth of smartphone data collected by the Cosmic Ray Extremely Distributed Observatory (CREDO) greatly surpasses the capabilities of manual analysis. So, efficient means of rejecting the non-cosmic-ray noise and identification of signals attributable to extensive air showers are necessary. To address these problems we discuss a Convolutional Neural Network-based method of artefact rejection and complementary method of particle identification based on common statistical classifiers as well… 

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