2 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes
Cen Mo, Liang Li
Fast and accurate muon reconstruction is crucial for neutrino telescopes to improve experimental sensitivity and enable online triggering. This paper introduces a hybrid-graph neur…
MuonSLab: A plastic scintillator based detector for muon measurement in the deep ocean
Jiacheng Wu, Weilun Huang, Ruike Cao +16
Atmospheric muons are important probes for studying primary cosmic rays and extensive air showers. Additionally, they constitute a significant background for many underground and d…
Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
Cen Mo, Fuyudi Zhang, Liang Li
TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino telescope to be located in the South China Sea. With a large detector volume and the use of advanced hy…
Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques
Yulei Zhang, Cen Mo, Xiang Chen +4
Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to…