PoS - Proceedings of Science
Volume 441 - XVIII International Conference on Topics in Astroparticle and Underground Physics (TAUP2023) - Neutrino Physics and Astrophysics
Machine learning-based waveform reconstruction at JUNO
G. Huang*  on behalf of the JUNO collaboration
Full text: pdf
Pre-published on: January 02, 2024
Published on: March 22, 2024
The analysis of the waveforms of photo-multiplier tube (PMT) is essential for high precision measurement of position and energy of particles' interaction in liquid scintillator (LS) detectors. JUNO is a next-generation large volume liquid scintillator neutrino experiment with a designed energy resolution of 3% @1 MeV. The accuracy of the reconstruction of the number of photoelectron (nPE) is one important key of achieving the best energy resolution. This work introduces the machine learning-based nPE estimation methods, including supervised learning depended on electronic simulation and data-driven weakly supervised learning. The calibration parameters of LS and PMT responses are used to generate training waveforms for supervised learning. The photon counting performances of different methods will be presented.
DOI: https://doi.org/10.22323/1.441.0265
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