Deep learning to classify ultra-high-energy cosmic rays by means of PMT signals

0555681 - FZÚ 2022 RIV GB eng J - Journal Article
Carrillo-Perez, F. … Total 4 authors
Deep learning to classify ultra-high-energy cosmic rays by means of PMT signals.
Neural Computing & Applications. Roč. 33, č. 15 (2021), s. 9153-9169. ISSN 0941-0643. E-ISSN 1433-3058
Research Infrastructure: AUGER-CZ II - 90102
Keywords : convolutional neural networks * Monte Carlo * cosmic ray showers
OECD category: Particles and field physics
Impact factor: 5.102, year: 2021
Method of publishing: Limited access
https://link.springer.com/content/pdf/10.1007/s00521-020-05679-9.pdf

One of the most captivating problems being faced nowadays in Physics are ultra-high energy cosmic rays. They are high-energy radiations coming mainly from outside the Solar System, and when they enter Earth’s atmosphere, they produce a cascade of particles. This cascade of particles, named as extensive air shower, can be recorded by means of photomultiplier tubes in surface detectors, obtaining different recordings of the energy signal (since the air shower can hit one or more detectors). Based on these signals, different features can be obtained that might give an insight into which particle has caused the extensive air shower, which is of utmost importance for physicists.
Permanent Link: http://hdl.handle.net/11104/0330144