Machine learning transforms the inference of the nuclear equation of state

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments,astrophysical observations,and nuclear theory calculations.Advancing our understanding of the nuclear equation of state(EOS,which is one of the most important properties and of central interest in nuclear physics)has relied on various data produced from experiments and calculations.We review how machine learning is revolutionizing the way we extract EOS from these data,and summarize the challenges and opportunities that come with the use of machine learning.

The Brueckner-Hartree-Fock Equation of State for Nuclear Matter and Neutron Skin

The equation of state for nuclear matter is presented within the Brueckner Hartree-Fock （BHF） scheme, by using the realistic Argonne VI8 or Bonn B two-nucleon potentials plus their corresponding microscopic three-nucleon forces. It is then applied to calculate the properties of finite nuclei within a simple liquid-drop model, and we compare the calculated volume, surface, and Coulomb parameters with the empirical ones from the liquid drop model. Nuclear density distributions and charge radii in good agreement with the experimental data are obtained~ and we predict the neutron skin thickness of various nuclei.

Bayesian Inference of Nucleus Resonance and Neutron Skin

In this proceeding,some highlight results on the constraints of the nuclear matter equation of state(EOS)from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the Skyrme-Hartree-Fock model and its extension have been presented.Typically,the anti-correlation and positive correlations between the slope parameter and the value of the symmetry energy at the saturation density under the constraint of the neutron-skin thickness and the isovector giant dipole resonance have been discussed respectively.It’s shown that the Bayesian analysis can help to find a compromise for the“PREXII puzzle”and the“soft Tin puzzle”.The possible modifications on the constraints of lower-order EOS parameters as well as the relevant correlation when higher-order EOS parameters are incorporated as independent variables have been further illustrated.For a given model and parameter space,the Bayesian approach serves as a good analysis tool suitable for multi-messengers versus multi-variables,and is helpful for constraining quantitatively the model parameters as well as their correlations.

CSHINE for studies of HBT correlation in heavy ion reactions

The compact spectrometer for heavy ion experiment(CSHINE)is under construction for the study of isospin chronology via the Hanbury Brown–Twiss(HBT)particle correlation function and the nuclear equation of state of asymmetrical nuclear matter.The CSHINE consists of silicon strip detector(SSD)telescopes and large-area parallel-plate avalanche counters,which measure the light charged particles and fission fragments,respectively.In phase I,two SSD telescopes were used to observe 30 MeV/u 40Ar?197Au reactions.The results presented here demonstrate that hydrogen and helium were observed with high isotopic resolution,and the HBT correlation functions of light charged particles could be constructed from the obtained data.

Nuclear matter incompressibility effect on the cross-section of fusion reactions with a weakly bound projectile

Fusion reactions with a weakly bound projectile are studied using the double-folding model along with a repulsive interaction modifying term. Using this modified potential, including nuclear matter incompressibility effects, the fusion reaction cross sections and suppression parameters are calculated for 9Be＋209Bi, 208spb, 29Si and 27A1 reactions. The results show that applying these effects at agreement between the calculated and experimental cross sections parameter. energies near the Coulomb barrier improves the and modifies the mean values of the suppression