1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
Fu-Peng Li, Long-Gang Pang, Guang-You Qin
The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simul…
Global polarization of hyperons and its sensitivity to equations of state in low-energy heavy-ion collisions
Cong Yi, Shi Pu, Long-Gang Pang +2
Significant global polarization of hyperons along the direction of the orbital angular momentum has been measured in non-central heavy-ion collisions where the equation of stat…
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…
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…
Nuclear equation of state at finite using deep learning assisted quasi-parton model
Fu-Peng Li, Long-Gang Pang, Guang-You Qin
To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential () remains a challenging yet essential goal in the study of QCD matter under ex…