Sequential networks for cosmic ray simulations
| dc.contributor.author | Sampathkumar,Pranav | |
| dc.contributor.author | Alves Junior,Augusto Antonio | |
| dc.contributor.author | Pierog,Tanguy | |
| dc.contributor.author | Ulrich,Ralf | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:29Z | |
| dc.date.available | 2022-09-28T17:10:29Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | A hybrid model of generating cosmic ray showers based on neural networks is presented. We show that the neural network learns the solution to the governing cascade equation in one dimension. We then use the neural network to generate the energy spectra at every height slice. Pitfalls of training to generate a single height slice is discussed, and we present a sequential model which can generate the entire shower from an initial table. Errors associated with the model and the potential to generate the full three dimensional distribution of the shower is discussed. | en |
| dc.identifier.doi | 10.18420/inf2022_41 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39541 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Sequential Neural Networks | |
| dc.subject | Astroparticle Physics | |
| dc.subject | Monte Carlo Simulations | |
| dc.title | Sequential networks for cosmic ray simulations | en |
| gi.citation.endPage | 506 | |
| gi.citation.startPage | 499 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Workshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro) |
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