{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T10:41:14Z","timestamp":1768300874354,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T00:00:00Z","timestamp":1599436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTI2018-101674-B-I00"],"award-info":[{"award-number":["RTI2018-101674-B-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PA2017-85197-P"],"award-info":[{"award-number":["PA2017-85197-P"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["404993\/2016-8"],"award-info":[{"award-number":["404993\/2016-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.<\/jats:p>","DOI":"10.3390\/e22090998","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:11:51Z","timestamp":1599523911000},"page":"998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Composition Classification of Ultra-High Energy Cosmic Rays"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3220-9389","authenticated-orcid":false,"given":"Luis Javier","family":"Herrera","sequence":"first","affiliation":[{"name":"Computer Architecture and Technology Department, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos Jos\u00e9","family":"Todero Peixoto","sequence":"additional","affiliation":[{"name":"Department of Basic Science and Environment, University of S\u00e3o Paulo, Lorena - SP 12602-810, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5434-4253","authenticated-orcid":false,"given":"Oresti","family":"Ba\u00f1os","sequence":"additional","affiliation":[{"name":"Computer Architecture and Technology Department, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Miguel","family":"Carceller","sequence":"additional","affiliation":[{"name":"Theoretical and Cosmos Physics Department, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0974-4092","authenticated-orcid":false,"given":"Francisco","family":"Carrillo","sequence":"additional","affiliation":[{"name":"Computer Architecture and Technology Department, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9918-3238","authenticated-orcid":false,"given":"Alberto","family":"Guill\u00e9n","sequence":"additional","affiliation":[{"name":"Computer Architecture and Technology Department, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"The Pierre Auger Collaboration (2015). Measurement of the cosmic ray spectrum above 4 \u00d7 1018 eV using inclined events detected with the Pierre Auger Observatory. J. Cosmol Astropart. P, 2015, 049.","DOI":"10.1088\/1475-7516\/2015\/08\/049"},{"key":"ref_2","unstructured":"Gaisser, T.K. (1990). Cosmic Rays and Particle Physics, Cambridge University Press."},{"key":"ref_3","unstructured":"The Pierre Auger Collaboration (2014). Depth of maximum of air-shower profiles at the Pierre Auger Observatory. I. Measurements at energies above 1017.8 eV. Phys. Rev. D, 90, 122005."},{"key":"ref_4","unstructured":"The Pierre Auger Collaboration (2017). Inferences on mass composition and tests of hadronic interactions from 0.3 to 100 EeV using the water-Cherenkov detectors of the Pierre Auger Observatory. Phys. Rev. D, 96, 122003."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1051\/jphysrad:0193900100103900","article-title":"Les grandes gerbes de rayons cosmiques","volume":"10","author":"Auger","year":"1939","journal-title":"J. Phys. Radium"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.astropartphys.2019.03.001","article-title":"Deep learning techniques applied to the physics of extensive air showers","volume":"111","author":"Bueno","year":"2019","journal-title":"Astropart. Phys."},{"key":"ref_7","unstructured":"Guill\u00e9n, A., Todero, C., Mart\u00ednez, J.C., and Herrera, L.J. (2018, January 26\u201328). A Preliminary Approach to Composition Classification of Ultra-High Energy Cosmic Rays. Proceedings of the 3rd International Conference on: Applied Physics, System Science and Computers (APSAC 2018), Lectures Notes in Electrical Engineering, Dubrovnik, Croatia."},{"key":"ref_8","unstructured":"Heck, D., Knapp, J., Capdevielle, J., Schatz, G., and Thouw, T. (1998). CORSIKA: A Monte Carlo Code to Simulate Extensive Air Showers, Forschungszentrum Karlsruhe GmbH. Technical report; 51.02.03; LK 01; Wissenschaftliche Berichte, FZKA-6019 (Februar 98)."},{"key":"ref_9","unstructured":"Institute for Nuclear Physics (IKP) (2020, September 03). CORSIKA\u2014An Air Shower Simulation Program. Available online: https:\/\/www.ikp.kit.edu\/corsika\/index.php."},{"key":"ref_10","unstructured":"Koller, D., and Sahami, M. (1996, January 3\u20136). Toward optimal feature selection. Proceedings of the Thirteenth International Conference on Machine Learning (ICML\u201996), Bari, Italy."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Herrera, L.J., Pomares, H., Rojas, I., Verleysen, M., and Guill\u00e9n, A. (2006, January 10\u201314). Effective input variable selection for function approximation. Proceedings of the 16th International Conference on Artificial Neural Networks, ICANN\u20192006\u2013LNCS 4131, Athens, Greece.","DOI":"10.1007\/11840817_5"},{"key":"ref_12","first-page":"2033","article-title":"Firmness prediction in Prunus persica \u2018Calrico\u2019 peaches by visible\/short-wave near infrared spectroscopy and acoustic measurements using optimised linear and non-linear chemometric models","volume":"111","author":"Lafuente","year":"2014","journal-title":"J. Sci. Food Agric."},{"key":"ref_13","unstructured":"Upasana, R., and Chouhan Usha, V.N. (2020). Comparative study of machine learning approaches for classification and prediction of selective caspase-3 antagonist for Zika virus drugs. Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Del Falco, I., and De Pietro, G.S. (2020). Evaluation of artificial intelligence techniques for the classification of different activities of daily living and falls. Neural Comput. Appl.","DOI":"10.1007\/s00521-018-03973-1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qin, P., and Shi, X. (2020). Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal. Entropy, 22.","DOI":"10.3390\/e22080852"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fanjul-V\u00e9lez, F., Pamp\u00edn-Su\u00e1rez, S., and Arce-Diego, J.L. (2020). Application of Classification Algorithms to Diffuse Reflectance Spectroscopy Measurements for Ex Vivo Characterization of Biological Tissues. Entropy, 22.","DOI":"10.3390\/e22070736"},{"key":"ref_17","unstructured":"The FCC Collaboration (2019). FCC-hh: The Hadron Collider. Eur. Phys. J. Spec. Top., 228, 755\u20131107."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"014018","DOI":"10.1103\/PhysRevD.83.014018","article-title":"Monte Carlo treatment of hadronic interactions in enhanced Pomeron scheme: QGSJET-II model","volume":"83","author":"Ostapchenko","year":"2011","journal-title":"Phys. Rev. D"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5710","DOI":"10.1103\/PhysRevD.50.5710","article-title":"SIBYLL: An event generator for simulation of high energy cosmic ray cascades","volume":"50","author":"Fletcher","year":"1994","journal-title":"Phys. Rev. D"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"034906","DOI":"10.1103\/PhysRevC.92.034906","article-title":"EPOS LHC: Test of collective hadronization with data measured at the CERN Large Hadron Collider","volume":"92","author":"Pierog","year":"2015","journal-title":"Phys. Rev. C"},{"key":"ref_21","unstructured":"Fesefeldt, H. (1985). The Simulation of Hadronic Showers: Physics and Applications, Cern Libraries."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ferrari, A., Sala, P., Fasso, A., and Ranft, J. (2005). FLUKA: A Multi-Particle Transport Code, Stanford Linear Accelerator Center (SLAC).","DOI":"10.2172\/877507"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/S0146-6410(98)00058-1","article-title":"Microscopic models for ultrarelativistic heavy ion collisions","volume":"41","author":"Bass","year":"1998","journal-title":"Prog. Part. Nucl. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nelson, W., Hirayama, H., and Rogers, D. (1985). EGS4 Code System (No. SLAC-265), Technical report.","DOI":"10.2172\/1453993"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Asif, A., Dawood, M., Jan, B., Khurshid, J., DeMaria, M., and Minhas, F.U.A.A. (2018). PHURIE: Hurricane intensity estimation from infrared satellite imagery using machine learning. Neural Comput. Appl.","DOI":"10.1007\/s00521-018-3874-6"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jamil, M., and Zeeshan, M. (2018). A comparative analysis of ANN and chaotic approach-based wind speed prediction in India. Neural Comput. Appl.","DOI":"10.1007\/s00521-018-3513-2"},{"key":"ref_27","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv Preprint."},{"key":"ref_29","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the KDD \u201916, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Scholkopf, B., and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1162\/089976603321891855","article-title":"Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel","volume":"15","author":"Keerthi","year":"2003","journal-title":"Neural Comput."},{"key":"ref_33","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","unstructured":"Cover, T.M., and Thomas, J.A. (2006). Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing), Wiley-Interscience."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"Kraskov","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","article-title":"Normalized Mutual Information Feature Selection","volume":"20","author":"Tesmer","year":"2009","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/9\/998\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:44Z","timestamp":1760177264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/9\/998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,7]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["e22090998"],"URL":"https:\/\/doi.org\/10.3390\/e22090998","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,7]]}}}