Abstract
In this paper, we use artificial neural networks (ANNs) techniques to reconstruct the mass composition of high energy cosmic rays. We train artificial neural networks using a high-performance computing cluster with 12 Nvidia Tesla V100 GPUs from the Laboratorio Nacional de Supercómputo del Sureste de México (LNS), and a database of approximately 4.8 million Monte Carlo (MC) simulations of extensive air showers (EAS) using the hadronic interaction model Sibyll 2.3 with two primaries: Protons and Irons, between the energy ranges of 1017 to 1019 eV. The longitudinal development profile of EAS produced by ultra-high energy cosmic rays carries physical information related to the interaction properties of the primary particles with atmospheric nuclei. We extract from the MC values of the longitudinal profile of air showers trough atmospheric depth on different energy ranges, the variable called Xmax (depth of EAS maximum development), which is strongly correlated with the composition of the primary cosmic ray, in order to predict Xmax values for very high-energy cosmic rays by using ANNs. These methods can be used to train a neural network with real EAS events and predict outcomes where statistical limitations with normal means cannot say much.
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The authors thankfully acknowledge computer resources, technical advice and support provided by Laboratorio Nacional de Supercómputo del Sureste de México (LNS), a member of the CONACYT national laboratories.
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Varela, E., Gabriel, I., Quiroz, A., Báez, L.A., Salazar, H., Villaseñor, L. (2019). Artificial Neural Networks for the Study of Cosmic Rays. In: Ruiz, P., Agredo-Delgado, V. (eds) Human-Computer Interaction. HCI-COLLAB 2019. Communications in Computer and Information Science, vol 1114. Springer, Cham. https://doi.org/10.1007/978-3-030-37386-3_9
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DOI: https://doi.org/10.1007/978-3-030-37386-3_9
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