https://doi.org/10.1140/epjc/s10052-024-12779-6
Regular Article - Experimental Physics
Pulse shape discrimination technique for diffuse supernova neutrino background search with JUNO
1
School of Nuclear Science and Engineering, North China Electric Power University, 102206, Beijing, China
2
Institute of High Energy Physics, Chinese Academy of Sciences, 100049, Beijing, China
3
School of Physical Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
4
Institute of Physics and EC PRISMA+, Johannes Gutenberg Universität Mainz, Mainz, Germany
c
ligs@ihep.ac.cn
d
liyufeng@ihep.ac.cn
e
zel032@physics.ucsd.edu
Received:
3
January
2024
Accepted:
8
April
2024
Published online:
9
May
2024
Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This technique is particularly valuable for studies of the diffuse supernova neutrino background (DSNB), nucleon decay, and dark matter searches. This paper presents a detailed investigation of the PSD technique, applied in the DSNB search performed with the Jiangmen Underground Neutrino Observatory (JUNO). Instead of using conventional cut-and-count methods, we employ methods based on boosted decision trees and neural networks and compare their capability to distinguish the DSNB signals from the atmospheric neutrino neutral-current background events. The two methods demonstrate comparable performance, resulting in a 50–80% improvement in signal efficiency compared to a previous study performed for JUNO (An et al. [JUNO] in J Phys G 43(3):030401, 2016). Moreover, we study the dependence of the PSD performance on the visible energy and final state composition of the events and find a significant dependence on the presence/absence of 
C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.
© The Author(s) 2024
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Funded by SCOAP3.
