Study On Ground State Properties Of Nuclei By Heavy-ion Collisions And Machine Learning

The masses of～2500 nuclei have been measured experimentally;however,7000-10000 isotopes are predicted to exist in the nuclear landscape from H（Z=1）to Og（Z=118）based on various theoretical calculations.Exploring the mass of the remaining isotopes is a popular topic in nuclear physics.Machine learning has served as a powerful tool for learning complex representations of big data in many fields.We use Light Gradient Boosting Machine（Light GBM）,which is a highly efficient machine learning algorithm,to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses.Several characteristic quantities（e.g.,mass number and proton number）are fed into the Light GBM algorithm to mimic the patterns of the residualδ（Z,A）between the experimental binding energy and the theoretical one given by the liquid-drop model（LDM）,Duflo-Zucker（DZ28,also dubbed DZ）mass model,finite-range droplet model（FRDM2012,also dubbed FRDM）,as well as the Weizs（?）cker-Skyrme（WS4）model to refine these mass models.By using the experimental data of 80%of known nuclei as the training dataset,the root mean square deviations（RMSDs）between the predicted and the experimental binding energy of the remaining 20%are approximately 0.234,0.213,0.170,and 0.222 Me V for the Light GBM-refined LDM,DZ,WS4,and FRDM,respectively.These values are approximately 90%,65%,40%,and 60%smaller than those of the corresponding origin mass models.The RMSD for 66 newly measured nuclei that appeared in AME2020was also significantly improved.The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models.In addition,the two-neutron separation energies of several newly measured nuclei（e.g.,some isotopes of Ca,Ti,Pm,and Sm）predicted with Light GBM-refined mass models are also in good agreement with the latest experimental data.Light GBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei.Moreover,the correlation between the input characteristic quantities and the output can be interpreted by SHapley additive explanations（a popular explainable artificial intelligence tool）,which may provide new insights for developing theoretical nuclear mass models.The influence of quadrupole deformation on the projectile and target nucleus in²³⁸U+²³⁸ U collisions at beam energies of 0.4,1.0,and 1.5 Ge V/nucleon is investigated using the ultra relativistic quantum molecular dynamics（Ur QMD）model.The effect of quadrupole deformation is investigated using the Light GBM decision tree algorithm.Light GBM’s ability to identify nuclear deformation from particle spectra is demonstrated on an event-by-event basis by learning the two-dimensional（transverse momentum and rapidity）distributions of free protons,charged fragments（mass number A>1）,and chargedπmesons produced in ²³⁸U+²³⁸ U collisions with and without deformed initializations.Using the important characteristic analysis of Light GBM,the yield of charged fragments around target/projectile rapidities is found to be sensitive to the quadrupole deformation of both the projectile and the target.It can be used as a promising observable to probe quadrupole deformation with heavy-ion collision.