Inference of the Mass Composition of Cosmic Rays with Energies from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>18.5</mml:mn> </mml:msup> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>20</mml:mn> </mml:mrow> </mml:msup> <mml:mtext> </mml:mtext> <mml:mtext> </mml:mtext> <mml:mi>eV</mml:mi> </mml:mrow> </mml:math> Using the Pierre Auger Observatory and Deep Learning
A. Abdul Halim, P. Abreu, M. Aglietta, I. Allekotte, Kévin Almeida Cheminant, A. Almela, Roberto Aloisio, Jaime Álvarez-Muñiz, Juan Ammerman Yebra, Gioacchino Alex Anastasi, L. Anchordoqui, B. Andrada, Luciana Andrade Dourado, S. Andringa, L. Apollonio, C. Aramo, P. R. Araújo Ferreira, E. Arnone, Juan Carlos Arteaga Velázquez, P. Assis, Gael Flores Avila, Emanuele Avocone, Alena Bakalová, Felicia Barbato, Adriel Bartz Mocellin, Corinne Bérat, M. E. Bertaina, Gopal Bhatta, Marta Bianciotto, Peter L. Biermann, V. Binet, Kathrin Bismark, Teresa Bister, Jonathan Biteau, Jiří Blažek, C. Bleve, J. Blümer, M. Boháčová, Denise Boncioli, C. Bonifazi, L. Bonneau Arbeletche, Nataliia Borodai, J. Brack, P. G. Brichetto Orchera, F. L. Briechle, A. Bueno, S. Buitink, Mario Buscemi, M. Büsken, Anthony Bwembya, K. S. Caballero‐Mora, S. Cabana-Freire, Lorenzo Caccianiga, F. Campuzano, R. Caruso, A. Castellina, F. Catalani, G. Cataldi, Lorenzo Cazon, M. Cerda, Berenika Čermáková, A. Cermenati, J. A. Chinellato, J. Chudoba, L. Chytka, R. W. Clay, Agustín Cobos Cerutti, R. Colalillo, M. R. Coluccia, R. Conceição, Antonio Condorelli, G. Consolati, M. Conte, Fabio Convenga, D. Correia dos Santos, P. J. Costa, C. E. Covault, M. Cristinziani, C. S. Cruz Sanchez, S. Dasso, K. Daumiller, B. R. Dawson, R. M. de Almeida, Beatriz de Errico, J. de Jesús, S. J. de Jong, J. R. T. de Mello Neto, I. De Mitri, J. de Oliveira, Danelise de Oliveira Franco, F. de Palma, V. de Souza, Emanuele De Vito, A. Del Popolo, O. Deligny, N. Denner, L. Deval, A. di Matteo, A John Joel, M. Dobre
Abstract
We present measurements of the atmospheric depth of the shower maximum X_{max}, inferred for the first time on an event-by-event level using the surface detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the X_{max} distributions up to energies of 100 EeV (10^{20} eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the fluorescence detector data, we find evidence that the rate of change of the average X_{max} with the logarithm of energy features three breaks at 6.5±0.6(stat)±1(syst) EeV, 11±2(stat)±1(syst) EeV, and 31±5(stat)±3(syst) EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured X_{max} distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 and 100 EeV.