English

Improved initial colliding nuclei density profile method for QMD-type transport models

Nuclear Theory 2025-10-30 v2

Abstract

Accurate modeling of the initial density profile is essential for studying heavy-ion collisions (HICs) with a transport model. Within the framework of the quantum molecular dynamics (QMD)-type model, a novel method for generating nuclear density distributions based on a Fourier series expansion (FSE) is proposed. In this approach, the objective density distribution is expanded into a Fourier series to construct a filter function, which is then applied to select the randomly sampled nucleon coordinates in phase space to generate a three-dimensional nuclear density distribution that matches the desired profile. This new initialization method is further incorporated into the ultrarelativistic quantum molecular dynamics (UrQMD) model, and the bubble-like density distribution of 96^{96}Ru is constructed, showing good stability. Then, by simulating 96^{96}Ru+96^{96}Ru collisions at Elab=1500E_\mathrm{lab}=1500 MeV/nucleon with different equations of state (EoS) and initialization methods, the effects of the initialization method on the final state observables and the constrained information of EoS are analyzed. It is found that the maximum system density increases when the new FSE initialization method is adopted, and results in an enhanced collective flow. Moreover, a relatively stiff EoS with K0>280K_0>280 MeV is favored when adopting the Woods-Saxon initialization method, whereas an EoS of K0K_0 = 200-280 MeV is supported when using the FSE initialization. These results indicate that, within QMD-like transport models, the FSE filtering method provides a reliable means to sample nuclei with exotic density profiles, offering new insight to investigate nuclear structure and dense nuclear matter EoS through HICs.

Keywords

Cite

@article{arxiv.2509.19089,
  title  = {Improved initial colliding nuclei density profile method for QMD-type transport models},
  author = {Xilong Xiang and Manzi Nan and Pengcheng Li and Yongjia Wang and Ling Liu and Qingfeng Li},
  journal= {arXiv preprint arXiv:2509.19089},
  year   = {2025}
}
R2 v1 2026-07-01T05:52:14.110Z