PoS - Proceedings of Science
Volume 444 - 38th International Cosmic Ray Conference (ICRC2023) - Cosmic-Ray Physics (Direct, CRD)
Convolutional Neural Network Measurement of Non-Fiducial Electrons Cosmic-Rays Using the DAMPE Experiment.
 Dampe, 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)
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Pre-published on: July 25, 2023
Published on: —
Abstract
The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic Ray (CR) observatory with the aim, among others, to study Cosmic Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV range, we aim at increasing the acceptance by selecting events outside the fiducial volume. The complex topology of non-fiducial events require special treatment with sophisticated analysis tools. Therefore, we propose a Convolutional Neural Network (CNN) to identify non-fiducial CREs and reject background events, based on their interaction in DAMPE's calorimeter. In the following, we will present the aforementioned method in order to precisely identify such events.
DOI: https://doi.org/10.22323/1.444.0130
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