arXiv subjects

Zepeng Gao

Publications and source records attributed to Zepeng Gao.

16 recordsLinked to original sources

arXiv2025

Mechanism of the quasi-elastic scattering based on the dinuclear system concept

A unified description of full reaction channels in low-energy heavy-ion collisions is a great challenge. Although the theoretical models based on the dinuclear system (DNS) concept have been successfully employed in multinucleon transfer (MNT) reactions, the underestimation of the quasi-elastic (QE) channel results in unreliable description of few nucleon transfer, especially for the light reaction systems. In this work, the DNS-sysu model is improved by introducing the impact-parameter-dependent transition probabilities for a unified description of few nucleon and many nucleon transfer in MNT reactions. Extensive experimental data -- including reactions such as 40Ca, 58Ni, 64Ni, 136Xe, and 208Pb + 208Pb -- were compared with the model predictions. The calculated isotopic distributions, mass distributions, and charge distributions show good agreement with experimental measurements. The improved DNS-sysu model enables reasonable characterization and description of the QE/grazing collisions, notably resolving long-standing underestimation in the QE channel.

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arXiv2025

Concentrated valence nucleons transfer in heavy-ion collisions: implications for questing the stable superheavy elements

The multinucleon transfer process is regarded as a promising pathway for producing the stable superheavy elements. However, the underlying mechanism, especially the possible transfer channels for sailing to the ``island of stability'' are poorly known. In this work, the time-dependent Hartree-Fock theory is used to investigate the collision dynamics of $^{136}$Xe, $^{198}$Pt, $^{238}$U + $^{238}$U reactions. A novel reaction channel of the concentrated valence nucleons (CVN) transferring is found in the collisions heading on the tips of $^{238}$U. These nucleons are transferred with relatively short relaxation time and break the symmetry of nucleon exchange in the early reaction stage. In consequence, the mass equilibrium with relaxation time is deviated from the systematic behavior based on the macroscopic-microscopic potential energy surface. The CVN transfer channel shows promising prospect for producing neutron-rich superheavy nuclei. In this case, we also investigated the angular distributions of products from the CVN transfer channel in the reaction $^{238}$U + $^{238}$U with Tip-Side configuration, and the optimal detection angles are predicted.

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arXiv2025

Time-dependent random phase approximation for particle-number fluctuations and correlations in deep-inelastic collisions of $^{144}$Sm+$^{144}$Sm and $^{154}$Sm+$^{154}$Sm

The fluctuation-dissipation mechanism underlying non-equilibrium transport in low-energy heavy-ion reactions remains unclear. Although the time-dependent Hartree-Fock (TDHF) method provides a reasonable description of average reaction outcomes and one-body dissipation, it is known to significantly underestimate fluctuations of observables. The purpose of this work is to investigate deep-inelastic collisions of 144Sm+144Sm and 154Sm+154Sm with microscopic mean-field approaches and to show a predominant role of one-body dissipation as well as one-body fluctuations and correlation in low-energy heavy-ion reactions. Three dimensional TDHF calculations are carried out for 144Sm+144Sm at Ecm=500 MeV and 154Sm+154Sm at Ecm=485 MeV for a range of impact parameters with Skyrme SLy5 energy density functional. Backward time evolutions are performed as well to evaluate fluctuations and correlation in nucleon numbers within time-dependent random phase approximation (TDRPA). With TDRPA we calculate mass- and charge-number fluctuations, as well as the correlation between neutron and proton transfers, for each impact parameter. We demonstrate that TDRPA quantitatively reproduces the experimental σ_{AA}^2-TKEL distributions, whereas it systematically underestimates the charge fluctuation, σ_{ZZ}. The double-differential cross sections of reaction products are calculated, showing good agreement with the experimental data. We confirm a long-thought characteristic property that the closed-shell structure limits nucleon transfer at small energy losses, based on our microscopic TDHF and TDRPA calculations.

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arXiv2024

Bayesian uncertainty quantification for synthesizing superheavy elements

To improve the theoretical prediction power for synthesizing superheavy elements beyond Og, a Bayesian uncertainty quantification method is employed to evaluate the uncertainty of the calculated evaporation residue cross sections (ERCS) for the first time. The key parameters of the dinuclear system (DNS) model, such as the diffusion parameter $\textit{a}$, the damping factor $E_\mathrm{d}$, and the level-density parameter ratio $a_\mathrm{f}/a_\mathrm{n}$ are systematically constrained by the Bayesian analysis of recent ERCS data. One intriguing behavior is shown that the optimal incident energies (OIE) corresponding to the largest ERCS weakly depend on the fission process. We also find that these parameters are strongly correlated and the uncertainty propagation considering the parameters independently is not reasonable. The 2$σ$ confidence level of posterior distributions for $a = 0.586_{-0.002}^{+0.002}$ fm, $E_\mathrm{d} = 25.65_{-3.41}^{+3.43}$ MeV, and $a_\mathrm{f}/a_\mathrm{n} = 1.081_{-0.021}^{+0.021}$ are obtained. Furthermore, the confidence levels of the ERCS and OIE for synthesizing Z = 119 via the reactions($^{54}\mathrm{Cr}+^{243}\mathrm{Am}$), (${}^{50}\mathrm{Ti}+{}^{249}\mathrm{Bk}$), and (${}^{51}\mathrm{V}+{}^{248}\mathrm{Cm}$) are predicted. This work sets the stage for future analyses to explore the OIE and reaction systems for the synthesis of superheavy elements.

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arXiv2024

New insight into the N/Z and mass equilibration in heavy-ion collisions

The dynamics of N/Z and mass equilibration are investigated in the reactions 112,124Sn + 239Pu by employing the isospin-dependent quantum molecular dynamics model. It is found that N/Z and mass equilibration take place at different collision stages. The N/Z relaxation is observed in the approaching phase (from first contact to deepest contact) with a very short time, whereas interestingly we find for the first time that mass equilibration only takes place in the separation phase (from the deepest contact to re-separation), which are explained by investigating the dynamical asymmetry between the approaching and separation phases. The mass equilibration also could be clarified with a dynamical potential energy surface. Our results provide a new insight into the equilibration dynamics of the quantum systems.

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arXiv2024

Quantifying angular distributions in multinucleon transfer reactions with a semi-classical method

The multinucleon transfer (MNT) process in low-energy heavy ion collisions can be utilized to produce unknown nuclei far beyond the stability line. However, the reaction products exhibit broad angular and energy distributions, which could lower the experimental detection efficiency. We present a classical approach that employs a parameterized angular distribution to describe the complex issue. By analyzing limited experimental data on angular distribution, we proposed a three-parameter formula to calculate the angular distribution and identified the dependencies of the parameters. We also discuss the sensitivity of these parameters within this method. A comprehensive comparison with microscopic models and experimental data across a wide range of conditions is conducted. The proposed formula offers an efficient and straightforward way to determine the angular distribution of MNT products.

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arXiv2024

Shell effects on the drift and fluctuation in multinucleon transfer reactions

This study employs the dinuclear system model (DNS-sysu) to investigate drift and fluctuation mechanisms in 136Xe + 209Bi collisions above the Coulomb barrier. The DNS-sysu model demonstrated its effectiveness in providing reasonable descriptions of drift and fluctuation dynamics for the multinucleon transfer reaction at low energies. We observe temperature-induced changes in the shell effect, impacting nucleon transfer. At higher energies, the weakening constraint of the potential energy surface leads to a reversal in the evolution direction. Additionally, the consideration of shell corrections notably affects fragment distribution at low energies but diminishes for high-energy conditions. This research provides valuable insights into understanding the macroscopic manifestation of nucleon transfer in the multinucleon transfer reaction.

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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

Role of the isospin diffusion on cluster transfer in $^{12,14}$C + $^{209}$Bi reactions

Heavy-ion collisions at near-barrier energies provide a crucial pathway for investigating nucleon correlations and clustering structures. Recent experimental results showed that the valence neutrons in light projectiles obviously enhance the $α$ transfer. This finding is extremely puzzled and fascinating, because it violates the ground-state $Q$ value systematics unexpectedly. In this work, the time-dependent Hartree-Fock approach is utilized to investigate the cluster transfer. By comparing the reactions $^{12,14}$C + $^{209}$Bi, we discover that above puzzling behavior is because of the strong correlation between isospin diffusion and clustering. Our calculations clearly show that the equilibrium of neutron-to-proton ratio strongly inhibits the clustering. This work opens a prospect for investigating the clustering in open quantum system.

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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

A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation

Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we employ our metric to automatically search for the optimal hyper-parameter set, achieving superior performance compared to manually tuned sets across four common benchmarks. Codes will be available soon.

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arXiv2023

The optimal detection angles for producing N=126 neutron-rich isotones in the multinucleon transfer reactions

The challenge of isotopic identification over a wide angular distribution has limited the measurement of neutron-rich nuclei produced via the multinucleon transfer (MNT) process. To investigate the optimal detection angles for the N=126 isotones, we propose a method to construct the reasonable scattering angles of the MNT products in the dinuclear system (DNS-sysu) model. The reactions $^{136,144}$Xe + $^{208}$Pb are investigated. The calculated results are in rather good agreement with the available experimental data in the reaction $^{136}$Xe + $^{208}$Pb. The entrance channel effects on the scattering angle are investigated. It is found that the scattering angular distribution strongly depends on the isospin and the impact parameter of the collision system. The optimal angle ranges for detecting $N=126$ neutron-rich nuclides $^{204}$Pt, $^{203}$Ir, $^{202}$Os, and $^{201}$Re in the $^{136}$Xe + $^{208}$Pb reaction at the incident energy $E_{c.m.} = 526MeV$ are predicted. Our results suggest that the angle range $45^{\circ} \leqslant θ_{\mathrm{lab}} \leqslant 50^{\circ}$ is most favorable for detecting unknown N=126 isotones. Given the current difficulties in separating and identifying experimental MNT fragments, the results of this work could provide significant contributions to future experiments.

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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

Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient boosting machine (LightGBM), a modern machine-learning algorithm, we present a framework for inferring the density-dependent nuclear symmetry energy from observables in heavy-ion collisions on the event-by-event analysis. The ultrarelativistic quantum molecular dynamics (UrQMD) model simulations are used as training data. The symmetry energy slope parameter extracted with LightGBM event-by-event from test data also by UrQMD has an average spread of approximately 30~MeV from the truth, and is found to be robust against variations in model parameters. In addition, LightGBM can identify features that have the greatest effect on the physics of interest, thereby offering valuable insights. Our study suggests that the present framework can be a powerful tool and may offer a new paradigm to study the underlying physics in heavy-ion collisions.

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arXiv2021

Application of machine learning in the determination of impact parameter in the $^{132}$Sn+$^{124}$Sn system

Background: $^{132}$Sn+$^{124}$Sn collisions at the beam energy of 270 MeV$/$nucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing impact parameter is one of the important tasks in the experiment as it relates to many observables. Purpose: In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN) and the light gradient boosting machine (LightGBM), to determine impact parameter by analyzing either the charged particles spectra or several features simulated with events from the ultra-relativistic quantum molecular dynamics (UrQMD) model. Method: To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. Results: The mean absolute error $Δb$ between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, $Δb$ would increase to 0.8 fm. Conclusion: The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct impact parameter in experiment.

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arXiv2021

Machine learning the nuclear mass

Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 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 remains is a hot topic in nuclear physics. Machine learning has been served as a powerful tool in learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM) which is a highly efficient machine learning algorithm to predict the masses of unknown nuclei and to explore the nuclear landscape in neutron-rich side from learning the measured nuclear masses. Results: By using the experimental data of 80 percent of known nuclei as the training dataset, the root mean square deviation (RMSD) between the predicted and the experimental binding energy of the remaining 20% is about 0.234 MeV, 0.213 MeV, 0.170 MeV, and 0.222 MeV for the LightGBM-refined LDM, DZ, WS4, and FRDM models, respectively. These values are of about 90%, 65%, 40%, and 60% smaller than the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 is also significantly improved on the same foot. One-neutron and two-neutron separation energies predicted by these refined models are in consistence with several theoretical predictions based on various physical models. Conclusions: LightGBM can be used to refine theoretical nuclear mass models so as to 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 (SHAP, a popular explainable artificial intelligence tool), this may provide new insights on developing theoretical nuclear mass models.

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