53 citations · 62 across the 14 of their papers we have counts for
4 papers · 1 filter
Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate
Si-Wei Dai, Haowu Duan, Long-Gang Pang +4
Gluon saturation limits the growth of parton densities at small Bjorken- and is expected to be most pronounced in heavy nuclei. Yet quantitative extractions of the nuclear gluon…
Physics-Informed Global Extraction of the Universal Small- Dipole Amplitude
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +4
We extract the universal small- dipole scattering amplitude from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-typ…
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…
Melting of heavy quarkonia in QGP using deep neural networks
Mohammad Yousuf Jamal, Fu-Peng Li, Long-Gang Pang +1
Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework e…