Machine learning applications on event reconstruction and identification for ISS-CREAM

Monong Yu, Tyler B. Anderson, Yu Chen, Stéphane Coutu, Tyler LaBree, Jason T. Link, John W. Mitchell, S. A. Isaac Mognet, Scott Nutter, Kenichi Sakai, Jacob Smith

Research output: Contribution to journalConference articlepeer-review


A supervised machine learning algorithm is applied to the visual representations of the energy deposits in two orthogonal views of the calorimeter of ISS-CREAM. Convolutional Neural Networks (CNNs) backed by Tensorflow are used to calibrate the sampled energy of the calorimeter and reconstruct the total primary energy of cosmic rays (CR), as well as for CR identification. The CNN regression models are trained on detailed Monte Carlo simulated events reproducing the behavior of the ISS-CREAM instrument suite, and the results indicate that a calorimeter energy reconstruction resolution of as good as 25% is achieved. The energy sampled in the calorimeter is determined with a resolution as good as 8%. The CNN classification model can reach a CR identification accuracy of up to 93%. The CR primary energy reconstruction results from machine learning methods are consistent with a simple scaling of the sampled energy. The increased accuracy of this CNN energy reconstruction comes from the additional information of the longitudinal and lateral energy deposit profiles. This machine learning approach is widely applicable to a range of particle physics and astrophysics problems.

Original languageEnglish (US)
Article number061
JournalProceedings of Science
StatePublished - Mar 18 2022
Event37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany
Duration: Jul 12 2021Jul 23 2021

All Science Journal Classification (ASJC) codes

  • General


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