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Multiplier-less approach in the neural network trigger algorithm for a detection of cosmic rays
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SYSNO ASEP 0549098 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Multiplier-less approach in the neural network trigger algorithm for a detection of cosmic rays Author(s) Szadkowski, Z. (PL) Number of authors 1 Source Title Proceedings of 11th International Conference on Computational Intelligence and Communication Networks (CICN 2019). - Danvers : IEEE, 2019 - ISBN 978-1-5386-8440-5 Pages s. 13-18 Number of pages 6 s. Publication form Print - P Action International Conference on Computational Intelligence and Communication Networks (CICN 2019) /11./ Event date 03.01.2019 - 04.01.2019 VEvent location Honolulu, HI Country US - United States Event type WRD Language eng - English Country US - United States Keywords Pierre Auger Observatory ; neural networks ; FPGA Subject RIV BF - Elementary Particles and High Energy Physics OECD category Particles and field physics Research Infrastructure AUGER-CZ - 90038 - Fyzikální ústav AV ČR, v. v. i. EID SCOPUS 85075912952 DOI 10.1109/CICN.2019.8902417 Annotation Nowadays astrophysics is focused on understand the origin of the ultrahigh-energy cosmic rays (UHECR). Finding sources of UHECR is difficult, due to deflection of charged particles in intergalactic magnetic fields. This problem can be, however, avoided by detecting electrically neutral particles, such as neutrinos, which are created by the UHECR particles in interactions during propagation. Due to the very low cross section of the neutrinos, the detection technique requires a very sophisticated algorithm.Our trigger algorithm is based on an analysis of signal shapes by an artificial neural network (ANN). This approach can efficiently separate air showers which started at the top of the atmosphere. Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2022 Electronic address https://doi.org/10.1109/CICN.2019.8902417
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