https://doi.org/10.1140/epjc/s10052-024-13724-3
Regular Article - Experimental Physics
Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors
1
Wuyi University, 529020, Jiangmen, China
2
School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China
3
Institute of High Energy Physics, Chinese Academy of Sciences, 100049, Beijing, China
a
huanggh@wyu.edu.cn
b
liuzhen@ihep.ac.cn
c
luowm@ihep.ac.cn
Received:
15
July
2024
Accepted:
20
December
2024
Published online:
24
January
2025
Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolution in large liquid scintillator detectors with PMTs is the charge smearing of PMTs. This paper presents a machine-learning-based photon counting method for PMT waveforms and its application to the energy reconstruction, using the JUNO experiment as an example. The results indicate that leveraging the photon counting information from the machine learning model can partially mitigate the impact of PMT charge smearing and lead to a relative 2.0–2.8% improvement on the energy resolution in the energy range of [1, 9] MeV.
© The Author(s) 2025
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Funded by SCOAP3.
