219 citations · 364 across the 12 of their papers we have counts for
12 papers
Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +3
Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental…
Impact of Initial-State Nuclear and Sub-Nucleon Structures on Ultra-Central Puzzle in Heavy Ion Collisions
Qi Wang, Long-Gang Pang, Xin-Nian Wang
Hydrodynamic models fail to describe the near-equal ratio observed in ultra-central heavy-ion collisions, despite their success in other centrality classes. This discrepa…
A Novel Deep Learning Method for Detecting Nucleon-Nucleon Correlations
Yu-Jing Huang, Zhu Meng, Long-Gang Pang +1
This study investigates the impact of nucleon-nucleon correlations on heavy-ion collisions using the hadronic transport model SMASH in GeV +$^…
Is AI Robust Enough for Scientific Research?
Jun-Jie Zhang, Jiahao Song, Xiu-Cheng Wang +14
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant d…
Asymmetric jet shapes with 2D jet tomography
Yu-Xin Xiao, Yayun He, Long-Gang Pang +2
Two-dimensional (2D) jet tomography is a promising tool to study jet medium modification in high-energy heavy-ion collisions. It combines gradient (transverse) and longitudinal jet…
Solving Einstein equations using deep learning
Zhi-Han Li, Chen-Qi Li, Long-Gang Pang
Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric sys…