nucl-th digest · Jul 31, 2020

Far-from-equilibrium search for the QCD critical point

Travis Dore🇺🇸 · Emma McLaughlin🇺🇸 · Jacquelyn Noronha-Hostler🇺🇸

Initial conditions for relativistic heavy-ion collisions may be far from equilibrium (i.e. there are large initial contributions from the shear stress tensor and bulk pressure) but it is expected that on very short time scales the dynamics converge to a universal attractor that defines hydrodynamic behavior. Thus far, studies of this nature have only considered an idealized situation at LHC energies (high temperatures $T$ and vanishing baryon chemical potential $μ_{B} = 0$ ) but, in this work, we investigate for the first time how far-from-equilibrium effects may influence experimentally driven searches for the Quantum Chromodynamic critical point at RHIC. We find that the path to the critical point is heavily influenced by far from equilibrium initial conditions where viscous effects lead to dramatically different ${T, μ_{B}}$ trajectories through the QCD phase diagram. We compare hydrodynamic equations of motion with shear and bulk coupled together at finite $μ_{B}$ for both DNMR and phenomenological Israel-Stewart equations of motion and discuss their influence on potential attractors at finite $μ_{B}$ and their corresponding ${T, μ_{B}}$ trajectories.

nucl-thPRD(2020)·71 citations

A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction

Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations {\sc spacs}, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross sections in the proton induced spallation reactions. A systematic investigation have been performed for the measured proton induced spallation reactions of systems ranging from the intermediate to the heavy nuclei and the incident energy ranging from 168 MeV/u to 1500 MeV/u. By learning the residuals between the experimental measurements and the {\sc spacs} predictions, the BNN predicted results are in good agreement with the measured results. The established method is suggested to benefit the related researches in the nuclear astrophysics, nuclear radioactive beam source, accelerator driven systems, and proton therapy, etc.

nucl-thnucl-exCPC(2020)·17 citations

Friday·July 31, 2020

Far-from-equilibrium search for the QCD critical point

A Bayesian-Neural-Network Prediction for Fragment Production in Proton Induced Spallation Reaction

Affiliations