Machine learning applications on event reconstruction and identification for ISS-CREAM
Loading...
Links to Files
Permanent Link
Author/Creator ORCID
Date
2021-07-12
Type of Work
Department
Program
Citation of Original Publication
Yu, Monong et al.; Machine learning applications on event reconstruction and identification for ISS-CREAM; 37th International Cosmic Ray Conference (ICRC2021), 12 July, 2021; https://pos.sissa.it/395/061/pdf
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Subjects
Abstract
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.