5 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…
Search for the radiative leptonic decay using Deep Learning
BESIII Collaboration, M. Ablikim, M. N. Achasov +678
Using 20.3 of annihilation data collected at a center-of-mass energy of 3.773 with the BESIII detector, we report an improved search for the radiat…
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…