From the 1 of 6 linked papers with an AI index.
6 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
The paper introduces a mixed‑representation neural network that combines visual and sequential features to identify antineutrons in electromagnetic calorimeters, enabling direct me…
Hyperon-Nucleon Spectrometer
Xiaozhi Bai, Xu Cao, Zhe Cao +237
Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions…
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…
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…