arXiv subjects

Gao-Song Li

Publications and source records attributed to Gao-Song Li.

2 recordsLinked to original sources

arXiv2025

Neutral-current background induced by atmospheric neutrinos at large liquid-scintillator detectors: III. Comprehensive prediction for low energy neutrinos

Atmospheric neutrinos play a vital role in generating irreducible backgrounds in liquid-scintillator (LS) detectors via their neutral-current (NC) interactions with $^{12}$C nuclei. These interactions may affect a wide range of research areas from the MeV to GeV energy range, such as the reactor and geo neutrinos, diffuse supernova neutrino background (DSNB), dark matter, and nucleon decay searches. In this work, we extend our preceding paper, by conducting a first-time systematic exploration of NC backgrounds as low as the MeV region of reactor and geo neutrinos. We utilize up-to-date neutrino generator models from GENIE and NuWro, a TALYS-based nuclear deexcitation package and a GEANT4-based detector simulation toolkit for our complete calculation. Our primary focus is to predict the NC background for experimental searches of inverse-beta-decay signals below the 100 MeV visible energy. In order to have deeper understanding of the characteristics of atmospheric neutrino NC interactions in LS, we investigate the model dependence of NC background predictions by using various data-driven models, including the initial neutrino-nucleon interactions, nuclear ground-state structure, final-state interactions, nuclear deexcitation processes, and secondary interactions of final-state particles.

hep-ph↗

arXiv2024

Pulse shape discrimination technique for diffuse supernova neutrino background search with JUNO

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\% to 80\% improvement in signal efficiency compared to a previous study performed for JUNO~\cite{JUNO:2015zny}. 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 $^{11}$C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.

hep-ex↗