Abstract
The study of cosmic rays remains as one of the most challenging research fields in Physics. There are several reasons why it is a hot topic as it can reveal information on Galaxy life-cycle and the mystery of how these rays arrive to the Earth with such high energy values (beyond 10\(^{20}\) eV). The work presented in this paper tries to answer a first question regarding to the type of primary particle that hits the atmosphere. Starting from a subset of known variables, we will try to perform a classification using Deep Learning models. We also compare two different approaches: classical classification and continuous output (like in regression). The results obtained are quite encouraging as we were able to obtain high classification accuracy even with a very reduced subset of variables.
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Acknowledgements
This research has been possible thanks to the support of projects: FPA2015-70420-C2-2-R, FPA2017-85197-P and TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness –MINECO– and the European Regional Development Fund. –ERDF). The authors want to thank the help and advice given by Antonio Bueno and Juan Miguel Carceller from the Department of Theoretic Physics and Cosmos at the University of Granada.
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Guillén, A., Todero, C., Martínez, J.C., Herrera, L.J. (2019). A Preliminary Approach to Composition Classification of Ultra-High Energy Cosmic Rays. In: Ntalianis, K., Vachtsevanos, G., Borne, P., Croitoru, A. (eds) Applied Physics, System Science and Computers III. APSAC 2018. Lecture Notes in Electrical Engineering, vol 574 . Springer, Cham. https://doi.org/10.1007/978-3-030-21507-1_29
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DOI: https://doi.org/10.1007/978-3-030-21507-1_29
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