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nucl-th2026
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…
nucl-th2026
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…
nucl-th2026
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…