A deep learning method for event recognition in CALET data

The Calorimetric Electron Telescope (CALET) is a powerful tool to observe cosmic-ray electrons between 1 GeV and 20 TeV. Its 30 radiation-length calorimeter enables total containment of electron-induced showers up to TeV energies, yielding an energy resolution of ∼ 2% for these events. The CALET all-electron spectrum obtained using the first 7.5 years of data closely matches the one produced by the AMS-02, but in tension with DAMPE and Fermi-LAT in the 60 GeV - 2 TeV energy range. To investigate this tension, we explore the possibility of a bias in the event selection by developing an alternative classification method between electrons and protons using machine learning techniques instead of a deterministic algorithm. These unsupervised learning techniques are used to find clustering in the flight data events without training on simulated data. Here we present preliminary results from this analysis, and the performance of the trained method when applied to the simulated dataset.

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Publication Details

Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.0113
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
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article

A deep learning method for event recognition in CALET data

Koichi Sakai, K. Yoshida, J.F. Ormes, Kohta Asano et al.
Anomaly Detection Techniques and Applications
article

A deep learning method for event recognition in CALET data

Koichi Sakai, K. Yoshida, J.F. Ormes, Kohta Asano, S. Ricciarini, K. Munakata, Y. Asaoka, Wolfgang V Zober, A. Yoshida, Kazunori Kohri, C. Checchia, T.G. Guzik, Kohei Kasahara, G. A. deNolfo, S. Yanagita, G. Collazuol, Y. Katayose, T. Hams, M. Bongi, T. Terasawa, Mitsuhiro Mori, S. Okuno, T. Sakamoto, Andrew E. Bruno, E. Berti, L. Pacini, K. Yamaoka, A. Shiomi, M. H. Israel, J. Nishimura, Misao Sasaki, G. Bigongiari, K. Hibino, P. Betti, Holger Motz, R. Kataoka, K. Ebisawa, John Krizmanic, G. Castellini, Y. Shimizu, W. R. Binns, J. P. Wefel, P. Brogi, B. F. Rauch, Mojca Mattiazzi Ušaj, H. Krawczynski, Michela Negro, F. Stolzi, Anthony W. Ficklin, Adrien Picquenot, Y. Uchihori, N. Kawanaka, Yosui Akaike, M. Ichimura, S. Sugita, А.А. Моисеев, J. Kataoka, Shusaku Torii, Y. Kawakubo, S. Nakahira, Naoto Mori, M. L. Cherry, P. Maestro, P. Papini, K. Kobayashi, P.S. Marrocchesi, A. Messineo, S. Gonzi, Shinji Ozawa, N. Cannady, J. W. Mitchell, O. Adriani, Y. Tsunesada, П. Спиллантини, M. Takita, A. Sulaj, Hiroyuki Fuke, S. Miyake, E. Vannuccini, C. Kato, T. Tamura
article en

Abstract

The Calorimetric Electron Telescope (CALET) is a powerful tool to observe cosmic-ray electrons between 1 GeV and 20 TeV. Its 30 radiation-length calorimeter enables total containment of electron-induced showers up to TeV energies, yielding an energy resolution of ∼ 2% for these events. The CALET all-electron spectrum obtained using the first 7.5 years of data closely matches the one produced by the AMS-02, but in tension with DAMPE and Fermi-LAT in the 60 GeV - 2 TeV energy range. To investigate this tension, we explore the possibility of a bias in the event selection by developing an alternative classification method between electrons and protons using machine learning techniques instead of a deterministic algorithm. These unsupervised learning techniques are used to find clustering in the flight data events without training on simulated data. Here we present preliminary results from this analysis, and the performance of the trained method when applied to the simulated dataset.

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Anomaly Detection Techniques and Applications
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