From the 1 of 7 linked papers with an AI index.
8 papers
Further exploration of the machine-learning-based nuclear mass table
Liu Yaqi, Li Zhilong, Wang Yongjia +1
The authors apply machine‑learning‑refined nuclear mass models to predict recently measured nuclear masses, residual proton‑neutron interactions, and α‑decay energies, achieving si…
Machine learning the impact parameter in heavy-ion collisions at = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM
Xiaoqing Yue, Guojun Wei, Yongjia Wang +7
By generating heavy-ion collision data with the ultrarelativistic quantum molecular dynamics (UrQMD) model, a multiphase transport (AMPT) model, and the JAM model, the impact param…
Effects of the centrality determination method for the equation of state and nucleonic observables from Au+Au collisions at = 2.4 GeV
Xiaoqing Yue, Pengcheng Li, Yongjia Wang +2
Centrality determination remains one of the major sources of systematic uncertainty in intermediate-energy heavy-ion collision analyses, especially for probing the nuclear equation…
A novel filtering method for generating desired density profiles of colliding nuclei
Xilong Xiang, Manzi Nan, Pengcheng Li +3
Accurate modeling of the density profile is essential for studying heavy-ion collisions (HICs) with a transport model. Within the framework of the quantum molecular dynamics (QMD)-…
Probing the Three-dimension Emission Source and Neutron Skin via - Correlations in Heavy-Ion Collisions
Haojie Zhang, Junhuai Xu, Pengcheng Li +6
The Richardson-Lucy algorithm is applied to reconstruct the three-dimensional source function of identical pions from their two-particle correlation functions. The algorithm's perf…
Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Pengcheng Li, Manzi Nan, Haojie Zhang +8
Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic mo…