arXiv subjects

Yongjia Wang

Publications and source records attributed to Yongjia Wang.

At least 19 recordsLinked to original sources

arXiv2026

Effects of in-medium $NN$ inelastic cross sections and the high-momentum tail of nucleon momentum distributions on pion production in heavy-ion collisions

Pion production in intermediate-energy heavy-ion collisions (HICs) provides a sensitive probe of the nuclear equation of state and of the isospin dependence of reaction dynamics. In particular, pion production near threshold is strongly affected by the nucleon-nucleon ($NN$) inelastic cross sections and by the high-momentum components of the nucleon momentum distribution. To explore the influence of these two ingredients on pion production and charged-pion ratios, the in-medium $NN$ inelastic cross sections calculated within the relativistic Boltzmann-Uehling-Uhlenbeck transport theory and the short-range-correlation-induced high-momentum tail (HMT) are introduced into the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model. By simulating Au+Au collisions at intermediate energies, we find that the in-medium modification of the $NN$ inelastic cross sections suppresses the pion multiplicity by reducing the probability of $NΔ$ production in dense matter. The HMT, on the other hand, enhances the high-momentum components of nucleons and modifies the available energy in individual $NN$ collisions, thereby affecting $NN\rightarrow NΔ$ reactions and the subsequent pion production. With the simultaneous inclusion of these two effects, the pion yields measured by HADES and the $π^-/π^+$ ratio measured by FOPI can be reasonably reproduced. These results highlight the need to incorporate both in-medium reaction cross sections and short-range-correlation-induced high-momentum components consistently in transport-model studies of pion production in heavy-ion collisions.

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arXiv2026

Dynamical Coulomb effects on charged-pion HBT-radius splitting in central Au+Au collisions at few-GeV energies

The pair transverse momentum ($k_T$) and collision-energy ($\sqrt{s_{NN}}$) dependent difference between $π^{+}π^{+}$ and $π^{-}π^{-}$ femtoscopic correlations was observed by the HADES and STAR Collaborations. We investigate how much of the observed splitting can be accounted for by Coulomb dynamics in 0-10\% central Au+Au collisions at $\sqrt{s_{NN}}=2.42$-5.2 GeV. Using the UrQMD model and the CRAB programme, three transport scenarios that successively include baryon-baryon, baryon-meson, and meson-meson Coulomb interactions are considered. In a separate calculation based on the baryon-baryon reference, we reconstruct the time-dependent effective charge $Z_{\rm{eff}}(t)$ and radius $R_{\rm{eff}}(t)$ of the residual charged source and propagate each pion from its individual last strong-interaction point through the evolving field. The calculations reproduce the main $k_{T}$ and $\sqrt{s_{NN}}$ dependences of the radii. Baryon--meson Coulomb interactions generate most of the additional charge splitting in the microscopic calculation. The residual-source treatment also enhances the longitudinal and sideward radius ratios relative to the reference, whereas the outward ratio changes little, and the enhancement generally decreases with increasing $\sqrt{s_{NN}}$. Within the present framework, this contribution does not fully account for the data, motivating further investigation of the charged-source geometry and its interplay with strong-interaction dynamics.

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arXiv2026

A novel filtering method for generating desired density profiles of colliding nuclei

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)-type model, a novel method for generating desired nuclear density distributions based on Fourier series expansion is proposed. This new initialization method is further incorporated into the ultrarelativistic quantum molecular dynamics model, and the bubble-like density distribution of $^{96}$Ru is constructed. Then, by simulating $^{96}$Ru+$^{96}$Ru collisions at $E_{\rm lab}=1500$ MeV/nucleon with different equations of state (EoS) and initialization methods, the effects of the initial density distribution on the final state observables and the constrained information of EoS are analyzed. It is found that $^{96}$Ru nuclei with a bubble density profile lead to an increased maximum compression during the collision, which in turn enhances the collective flow. Moreover, a relatively stiff EoS with $K_0>280$ MeV is favored for the conventional Woods-Saxon type density profile, whereas an EoS with $K_0$=200-280 MeV is supported when a bubble-like density profile is employed. These results demonstrate that the initial nuclear density distribution plays a non-negligible role in dynamical observables and EoS constraints. The proposed method thus provides a powerful tool for constructing exotic profiles and investigating nuclear structure effects in HICs.

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arXiv2026

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM

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 parameter ($b$) in Au+Au collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV is reconstructed using supervised learning and unsupervised learning in machine learning (ML). In supervised learning, the performance of ML algorithm is cross-checked by using data obtained from these three transport models. It is found that the typical mean absolute error (MAE) which measures the average magnitude of the absolute difference between the true and predicted $b$ is between 0.2-0.4 fm, even when training ML algorithm with data generated from one model but testing with data from others. While the conventional method (i.e., a polynomial fit to multiplicity as a function of $b$) only works for data generated from the same model. In the classification task, the present ML-based method also shows significantly superior results compared to the traditional approach. In unsupervised learning, the K-means clustering algorithm is used to partition collision events directly from experimental-style observables, showing that the algorithm autonomously identifies six clusters corresponding to different centrality classes without relying on predefined model-based binning. Our study demonstrates the strong robustness of using an ML algorithm trained on transport-model data for impact-parameter determination, and indicates that this method has the potential to be generalized to handle real experimental data.

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arXiv2026

Effects of the centrality determination method for the equation of state and nucleonic observables from Au+Au collisions at $\sqrt{s_{NN}}$ = 2.4 GeV

Centrality determination remains one of the major sources of systematic uncertainty in intermediate-energy heavy-ion collision analyses, especially for probing the nuclear equation of state (EoS) at supra-saturation densities. To quantitatively assess the uncertainties associated with different centrality determination methods and to investigate their effects on final-state EoS-sensitive observables. Within the ultra-relativistic quantum molecular dynamics (UrQMD) model, Au+Au collisions at $\sqrt{s_{NN}}$=2.4 GeV are performed within a soft and a hard EoS. Event centrality is determined using the multiplicity of all charged particles ($M_\mathrm{ch}$) and two impact parameter-based centrality filters, one based on a geometrical interpretation and the other based on the Glauber Monte Carlo (MC) model, denoted as $b_{f}$ and $b_{r}$, respectively. It is shown that there exist significant differences between the real impact parameter distributions of event samples selected by $M_\mathrm{ch}$, $b_{f}$, and $b_{r}$, particularly between $M_\mathrm{ch}$ and $b_{r}$. When the $b_{f}$ is employed, uncertainties associated with centrality selection have a weaker influence on observables than the effects induced by the EoS. In contrast, when the $b_{r}$ is used, the influence of centrality-related uncertainties becomes more pronounced than that of the EoS. These results demonstrate that a rigorous and consistent mapping between $M_\mathrm{ch}$ and impact parameter is essential to impose quantitative constraints on the high-density nuclear EoS. Furthermore, our study indicates that the geometrical interpretation of centrality remains valid and consistent with dynamical multiplicity selection, whereas the Glauber MC-based centrality determination becomes unreliable at the investigated energy.

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arXiv2025

Probing the Three-dimension Emission Source and Neutron Skin via $π$-$π$ Correlations in Heavy-Ion Collisions

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 performance is first evaluated through simulations with Gaussian-type initial source functions. Its imaging quality and robustness are further demonstrated with experimental data from Au+Au collisions at 1.23 A GeV, collected by the HADES Collaboration. Additionally, using UrQMD simulations of Pb+Pb collisions at 1.5 A GeV, we show that the deblurred source functions exhibit sensitivity to the initial neutron skin thickness of the colliding nuclei. This highlights the potential of the Richardson-Lucy algorithm as a tool for probing the neutron density distribution in heavy nuclei.

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arXiv2025

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions

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 model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $π^{-}/π^{+}$, the two-pion momentum correlation function, and the emission source dimension. It is found that the $π^{-}/π^{+}$ ratio is sensitive to the initial neutron density distribution and the impact parameter, especially for collisions at large impact parameter. However, the charge splitting in the correlation functions between positively $π^{+}π^{+}$ and negatively $π^{-}π^{-}$, as well as the source radii and volumes extracted exhibit a stronger dependence on the initial neutron density distribution, but a weaker dependence on the impact parameter. The present study highlights that $π^{+}π^{+}$ and $π^{-}π^{-}$ correlation functions in heavy-ion collisions could be used to probe the initial neutron density distribution of nuclei.

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arXiv2025

Bayesian analysis of properties of nuclear matter with the FOPI experimental data

Based on the ultra-relativistic quantum molecular dynamics (UrQMD) transport model, combined with experimental data of directed flow, elliptic flow, and nuclear stopping power measured by FOPI in $\rm ^{197}Au+^{197}Au$ collisions at beam energies ($E_{lab}$) of 0.25 and 0.4 GeV/nucleon, the incompressibility of the nuclear equation of state $K_0$, the nucleon effective mass $m^*$, and the in-medium correction factor ($F$, with respect to free-space values) on the nucleon-nucleon elastic cross sections are studied by Bayesian analysis. It is found that both $m^*$ and $F$ can be tightly constrained with the uncertainty $\le$ 15\%, however, $K_0$ cannot be constrained tightly. We deduce $m^*/m_0 = 0.78^{+0.09}_{-0.10}$ and $F = 0.75^{+0.08}_{-0.07}$ with experimental data at $E_{lab}$ = 0.25 GeV/nucleon, and the obtained values increased to $m^*/m_0 = 0.88^{+0.03}_{-0.03}$ and $F = 0.88^{+0.06}_{-0.07}$ at $E_{lab}$ = 0.4 GeV/nucleon. The obtained results are further verified with rapidity-dependent flow data.

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arXiv2025

DESIL: Detecting Silent Bugs in MLIR Compiler Infrastructure

MLIR (Multi-Level Intermediate Representation) compiler infrastructure provides an efficient framework for introducing a new abstraction level for programming languages and domain-specific languages. It has attracted widespread attention in recent years and has been applied in various domains, such as deep learning compiler construction. Recently, several MLIR compiler fuzzing techniques, such as MLIRSmith and MLIRod, have been proposed. However, none of them can detect silent bugs, i.e., bugs that incorrectly optimize code silently. The difficulty in detecting silent bugs arises from two main aspects: (1) UB-Free Program Generation: Ensures the generated programs are free from undefined behaviors to suit the non-UB assumptions required by compiler optimizations. (2) Lowering Support: Converts the given MLIR program into an executable form, enabling execution result comparisons, and selects a suitable lowering path for the program to reduce redundant lowering pass and improve the efficiency of fuzzing. To address the above issues, we propose DESIL. DESIL enables silent bug detection by defining a set of UB-elimination rules based on the MLIR documentation and applying them to input programs to produce UB-free MLIR programs. To convert dialects in MLIR program into the executable form, DESIL designs a lowering path optimization strategy to convert the dialects in given MLIR program into executable form. Furthermore, DESIL incorporates the differential testing for silent bug detection. To achieve this, it introduces an operation-aware optimization recommendation strategy into the compilation process to generate diverse executable files. We applied DESIL to the latest revisions of the MLIR compiler infrastructure. It detected 23 silent bugs and 19 crash bugs, of which 12/14 have been confirmed or fixed

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arXiv2024

Isospin-dependent in-medium nucleon-Delta elastic cross section

In heavy-ion collisions at intermediate energies, the production and propagation of $Δ$ particles are crucial to understanding the nuclear equation of state and inferring the properties of nuclear matter at high densities. Based on the self-consistent relativistic Boltzmann-Uehling-Uhlenbeck (RBUU) transport theory, by introducing the isovector $ρ$ meson exchange into the effective Lagrangian and adopting the density-dependent coupling constants, the detailed expressions for isospin-dependent in-medium $NΔ\rightarrow NΔ$ elastic cross sections $σ_{NΔ}^{*}$ have been calculated. The energy and density dependence of the isospin-related $σ_{NΔ}^{*}$ as well as the total contributions of $σ$, $ω$ and $ρ$ meson fields are analyzed. It is found that the total $σ_{NΔ}^{*}$ has a sensitive center-of-mass energy dependence at lower energies while exhibiting a slight increase as the center-of-mass energy increases. The isospin effect between different isospin-separated channels weakens as the energy and/or density increases. The isospin effect on the density- and energy-dependent in-medium $NΔ$ elastic cross sections is dominantly caused by the delicate balance of the isovector $ρ$ meson exchange.

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arXiv2023

Constraining the Woods-Saxon potential in fusion reactions based on the neural network

The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an important role in the synthesis of superheavy elements. In this work, the neural network, which combines with the calculations of the fusion cross sections via the Hill-Wheeler formula, is developed to optimize the parameters of the Woods-Saxon potential by comparing the experimental values. The correlations between the parameters of Woods-Saxon potential and the reaction partners, which can be quantitatively fitted to a sigmoid-like function with the mass numbers, have been displayed manifestly for the first time. This study could promote the accurate estimation of nucleus-nucleus interaction potential in low energy heavy-ion collisions.

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arXiv2023

Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning

In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree based machine-learning algorithm, is used to study the fusion cross section (CS) of heavy-ion reaction. Several basic quantities (e.g., mass number and proton number of projectile and target) and the CS obtained from phenomenological formula are fed into the LightGBM algorithm to predict the CS. It is found that, on the validation set, the mean absolute error (MAE) which measures the average magnitude of the absolute difference between $log_{10}$ of the predicted CS and experimental CS is 0.129 by only using the basic quantities as the input, this value is smaller than 0.154 obtained from the empirical coupled channel model. MAE can be further reduced to 0.08 by including an physical-informed input feature. The MAE on the test set (it consists of 280 data points from 18 reaction systems that not included in the training set) is about 0.19 and 0.53 by including and excluding the physical-informed feature, respectively. We further verify the LightGBM predictions by comparing the CS of $^{ 40,48}{\rm Ca }$+$^{78}{\rm Ni}$ obtained from the density-constrained time-dependent Hartree-Fock approach. Our study demonstrates the importance of physical information in predicting fusion cross section of heavy-ion reaction with machine learning.

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arXiv2023

Studies of nuclear equation of state with the HIRFL-CSR external-target experiment

The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of state of nuclear matter (nEOS) with heavy ion collisions at a few hundreds MeV/u beam energies. Based on Geant 4 packages, the fast simulations of the detector responses to the collision events generated using transport model are conducted. The overall performance of CEE, including spatial resolution of hits, momentum resolution of tracks and particle identification ability has been investigated. Various observables proposed to probe the nEOS, such as the production of light clusters, $\rm t/^3He$ yield ratio, the radial flow, $π^{-}/π^{+}$ yield ratio and the neutral kaon yields, have been reconstructed. The feasibility of studying nEOS beyond the saturation density via the aforementioned observables to be measured with CEE has been demonstrated.

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arXiv2023

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.

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arXiv2023

Impacts of momentum dependent interaction, symmetry energy and near-threshold $NN\to NΔ$ cross sections on isospin sensitive flow and pion observables

Based on the ultra-relativistic quantum molecular dynamics (UrQMD) model, the impacts of momentum dependent interaction, symmetry energy and near-threshold $NN\to NΔ$ cross sections on isospin sensitive collective flow and pion observables are investigated. Our results confirm that the elliptic flow of neutrons and charged particles, i.e. $v_2^n$ and $v_2^{ch}$, are sensitive to the strength of momentum dependence interaction and the elliptic flow ratio, i.e., $v_2^n/v_2^{ch}$, is sensitive to the stiffness of symmetry energy. For describing the pion multiplicity near the threshold energy, accurate $NN\to NΔ$ cross sections are crucial. With the updated momentum dependent interaction and $NN\to NΔ$ cross sections in UrQMD model, seven observables, such as directed flow and elliptic flow of neutrons and charged particles, the elliptic flow ratio of neutrons to charged particles, charged pion multiplicity and its ratio $π^-/π^+$, can be well described by the parameter sets with the slope of symmetry energy from 5 MeV to 70 MeV. To describe the constraints of symmetry energy at the densities probed by the collective flow and pion observables, the named characteristic density is investigated and used. Our analysis found that the flow characteristic density is around 1.2$ρ_0$ and pion characteristic density is around 1.5$ρ_0$, and we got the constrains of symmetry energy at characteristic densities are $S(1.2ρ_0)=34\pm 4$ MeV and $S(1.5ρ_0)=36\pm 8$ MeV. These results are consistent with previous analysis by using pion and flow observable with different transport models, and demonstrate a reasonable description of symmetry energy constraint should be presented at the characteristic density of isospin sensitive observables.

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arXiv2022

How Do Constraints of Nuclear Symmetry Energy Reconcile with Different Models?

By simultaneously describing the data of isospin sensitive nucleonic flow and pion observables, such as $v_2^n/v_2^{ch}$ and $π^-/π^+$, with ultra-relativistic quantum molecular dynamics (UrQMD) model, we got the symmetry energy at flow and pion characteristic densities which are $S(1.2ρ_0)=34\pm 4$ MeV and $S(1.5ρ_0)=36\pm 8$ MeV. Within the uncertainties, the constraints of symmetry energy at characteristic densities are consistent with the previous constraints by using other transport models. The consistency suggests that the reliable constraints on symmetry energy should be presented at the characteristic density of isospin sensitive observables. By using the constraints of symmetry energy at two different characteristic densities, the extrapolated value of $L$ is provided. Within $2σ$ uncertainty, the extrapolated value of $L$ is in $5-70$ MeV which is consistent with the recent combination analysis from PREX-II and astrophyiscs data. Further, the calculations with the constrained parameter sets can describe the data of charged pion multiplicities from S$π$RIT collaboration.

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arXiv2022

Elliptic flow in heavy-ion collisions at intermediate energy: the role of impact parameter, mean field potential, and collision term

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reverse tracing nucleons that are finally emitted at mid-rapidity (|$y_0$| < 0.1) in the entire reaction process, the time evolution of elliptic flow ($v_2$) of these traced nucleons produced in Au+Au collisions at beam energy of 0.4 GeV$/$nucleon with different impact parameters ($b$) is studied. The initial value of $v_2$ is positive and increases with $b$, then it decreases as time passes and tends to saturate at a negative value. It is found that nucleon-nucleon collisions always depress the value of $v_2$ (enhance the out-of-plane emission), while the nuclear mean field potential may slightly raise the value of $v_2$ during the expansion stage in peripheral reactions. The related density mostly probed by $v_2$ of nucleons at mid-rapidity is found to be $\sim$ 60% of the maximum density reached during the collisions.

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arXiv2022

Effects of Initial Density Fluctuations on Cumulants in Au + Au Collisions at $\sqrt{s_{NN}}$ = 7.7 GeV

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, the effect of initial density fluctuations on cumulants of the net-proton multiplicity distribution in Au + Au Collisions at $\sqrt{s_{NN}}$ = 7.7 GeV was investigated by varying the minimum distance $d_{\rm min}$ between two nucleons in the initialization. It was found that the initial density fluctuations increased with the decrease of $d_{\rm min}$ from 1.6 fm to 1.0 fm, and the influence of $d_{\rm min}$ on the magnitude of the net-proton number fluctuation in a narrow pseudorapidity window ($Δη\leq$ 4) was negligible even if it indeed affected the density evolution during the collision. At a broad pseudorapidity window ($Δη\geq$ 4), the cumulant ratios were enlarged when the initial density fluctuations were increased with the smaller value of $d_{\rm min}$, and this enhancement was comparable to that observed in the presence of the nuclear mean-field potential. Moreover, the enhanced cumulants were more evident in collisions with a larger impact parameter. The present work demonstrates that the fingerprint of the initial density fluctuations on the cumulants in a broad pseudorapidity window is clearly visible, while it is not obvious as the pseudorapidity window becomes narrow.

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