Contribution to a conference proceedings/Contribution to a book

GSI-2025-01361

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Novel Fitting Approach Based on a Neural Network for JUNO

Malyshkin, Y. (Corresponding author)GSI*

2025
EDP Sciences Les Ulis

Please use a persistent id in citations: doi:10.1051/epjconf/202533701219  doi:10.15120/GSI-2025-01350

Note: This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

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Contributing Institute(s):

1. FAIR Forschung NRW (FFN)

Research Program(s):

1. 612 - Cosmic Matter in the Laboratory (POF4-612) (POF4-612)

Experiment(s):

1. (JUNO Experiment at Jiangmen Underground Neutrino Observatory in China ( - ; other))

Appears in the scientific report 2025

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Database coverage:
; ; ; ; DOAJ Seal ; SCOPUS

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