4 papers
Learning transferable event representations for charmed baryon physics at BESIII
Kaixuan Huang, Yangu Li, Junpeng Zhao +6
Deep learning has become an essential tool in high-energy physics, where the ability to learn transferable event representations can significantly improve model generalization acro…
Antineutron reconstruction in electromagnetic calorimeters with mixed-representation learning
Yangu Li, Hongtian Yu, Yuyang Huang +7
A long-standing bottleneck in GeV-scale accelerator experiments lies in reconstructing long-lived neutral hadrons in conventional electromagnetic calorimeters (ECALs), where hadron…
Vision Calorimeter for High-Energy Particle Detection
Hongtian Yu, Yangu Li, Yunfan Liu +3
In high-energy physics, estimating anti-neutron parameters (position and momentum) using the electromagnetic calorimeter (EMC) is crucial but challenging. To conquer this challenge…
Vision Calorimeter for Anti-neutron Reconstruction: A Baseline
Hongtian Yu, Yangu Li, Mingrui Wu +8
In high-energy physics, anti-neutrons () are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties pr…