4 citations · 7 across the 6 of their papers we have counts for
4 papers · 1 filter
Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Jiamin Liu, Kai Zhou, Baoyi Chen
We use deep learning under Bayesian perspective to quantitatively extract the in-medium heavy quark (HQ) potential from bottomonium nuclear modification factors () measured…
Sensitivity of Neutron Star Observables to Transition Density in Hybrid Equation-of-State Models
N. K. Patra, Sk Md Adil Imam, Kai Zhou
We investigate how the transition density \(ρ_{tr}\) affects hybrid constructions of the neutron-star equation of state (EoS) in which a nucleonic description at low densities is m…
Neural-Network Holographic Model of the QCD Phase Transition under Lattice and HRG Constraints
De-Xing Zhu, Li-Qiang Zhu, Xun Chen +2
Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD cr…
Deep learning approaches to extract nuclear deformation parameters from initial-state information in heavy-ion collisions
Jun-Qi Tao, Yang Liu, Yu Sha +5
The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantit…