Volume 444 - 38th International Cosmic Ray Conference (ICRC2023) - Cosmic-Ray Physics (Direct, CRD)
Probing hadronic cross sections in the TeV - PeV regime with DAMPE through machine learning techniques
 on behalf of the DAMPE Collaboration, F. Alemanno, C. Altomare, Q. An, P. Azzarello, F.C.T. Barbato, P. Bernardini, X.J. Bi, I. Cagnoli, M.S. Cai, E. Casilli, E. Catanzani, J. Chang, D.Y. Chen, J.L. Chen, Z.F. Chen, Z.X. Chen, P. Coppin*, M.Y. Cui, T.S. Cui, Y.X. Cui, I. De Mitri, F. de Palma, A. Di Giovanni, M. Di Santo, Q. Ding, T.K. Dong, Z.X. Dong, G. Donvito, D. Droz, J.L. Duan, K.K. Duan, R.R. Fan, Y.Z. Fan, F. Fang, K. Fang, C.Q. Feng, L. Feng, M. Fernandez Alonso, J.M. Frieden, P. Fusco, M. Gao, F. Gargano, E. Ghose, K. Gong, Y.Z. Gong, D.Y. Guo, J.H. Guo, S.X. Han, Y.M. Hu, G.S. Huang, X.Y. Huang, Y.Y. Huang, M. Ionica, L.Y. Jiang, W. Jiang, Y.Z. Jiang, J. Kong, A. Kotenko, D. Kyratzis, S.J. Lei, W.L. Li, W.H. Li, X. Li, X.Q. Li, Y.M. Liang, C.M. Liu, H. Liu, J. Liu, S.B. Liu, Y. Liu, F. Loparco, C.N. Luo, M. Ma, P.X. Ma, T. Ma, X.Y. Ma, G. Marsella, M.N. Mazziotta, D. Mo, X.Y. Niu, X. Pan, A. Parenti, W.X. Peng, X.Y. Peng, C. Perrina, E. Putti-Garcia, R. Qiao, J.N. Rao, A. Ruina, Z. Shangguan, W.H. Shen, Z.Q. Shen, Z.T. Shen, L. Silveri, J.X. Song, M. Stolpovskiy, H. Su, M. Su, H.R. Sun, Z.Y. Sun, A. Surdo, X.J. Teng, A. Tykhonov, J.Z. Wang, L.G. Wang, S. Wang, X.L. Wang, Y.F. Wang, Y. Wang, Y.Z. Wang, D.M. Wei, J.J. Wei, Y.F. Wei, D. Wu, J. Wu, L.B. Wu, S.S. Wu, X. Wu, Z.Q. Xia, E.H. Xu, H.T. Xu, J. Xu, Z.H. Xu, Z.Z. Xu, Z.L. Xu, G.F. Xue, H.B. Yang, P. Yang, Y.Q. Yang, H.J. Yao, Y.H. Yu, G.W. Yuan, Q. Yuan, C. Yue, J.J. Zang, S.X. Zhang, W.Z. Zhang, Y. Zhang, Y.P. Zhang, Y. Zhang, Y.J. Zhang, Y.Q. Zhang, Y.L. Zhang, Z. Zhang, Z.Y. Zhang, C. Zhao, H.Y. Zhao, X.F. Zhao, C.Y. Zhou and Y. Zhuet al. (click to show)
*: corresponding author
Full text: pdf
Pre-published on: July 25, 2023
Published on: September 27, 2024
Abstract
Thanks to its large calorimeter, the DArk Matter Particle Explorer (DAMPE) satellite experiment is ideally suited for the direct detection of cosmic rays (CRs) up to the knee. At these TeV to PeV energies, the main uncertainty on the CR flux measurements comes from the hadronic cross sections, which are largely experimentally unconstrained. We developed novel machine learning (ML) tools that are able to probe the depth at which CRs inelastically interact inside the DAMPE experiment. Applying these techniques to 7 years of DAMPE data, and comparing the results to predictions made by CR simulation frameworks such as Geant4 and FLUKA, we demonstrate how DAMPE data can be used to constrain the hadronic cross sections. Our results thus provide an important step towards reducing the uncertainties of CR flux measurements. Additionally, they form a pathfinder for similar studies with future experiments.
DOI: https://doi.org/10.22323/1.444.0142
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