A Deep learning method for event recognition in CALET data

Files

ICRCpresentationAdrienPicquenot.pdf (2.86 MB)

Links to Files

https://indico.cern.ch/event/1258933/contributions/6486501/

Permanent Link

http://hdl.handle.net/11603/39806

Collections

UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
UMBC Faculty Collection

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Author/Creator ORCID

Date

2025-07-22

Type of Work

conference papers and proceedings
presentations (communicative events)
Text

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Citation of Original Publication

Rights

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

Subjects

Abstract

In this study we:- Apply unsupervised machine learning methods to classify CREs from CALET data. the goal is to minimize dependence on MC simulations and pursue a data-driven classification.- We evaluate algorithm performance using simulations, then apply the optimized models directly to real data.