Machine-Learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors

TL;DR

This paper introduces a machine learning method for photon counting in PMT waveforms that improves energy resolution in large liquid scintillator detectors, demonstrated with the JUNO experiment, by mitigating charge smearing effects.

Contribution

The paper presents a novel machine learning-based photon counting technique for PMT waveforms, enhancing energy resolution in liquid scintillator detectors beyond traditional methods.

Findings

Achieved a 2.0% to 2.8% improvement in energy resolution.

Mitigated effects of PMT charge smearing using machine learning.

Validated approach with the JUNO experiment data.

Abstract

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% to 2.8% improvement on the energy resolution in the energy range of [1, 9] MeV.