arXiv subjects

Yuanfang Wu

Publications and source records attributed to Yuanfang Wu.

At least 19 recordsLinked to original sources

arXiv2026

Understanding the Intermittency Signal in RHIC-STAR Data through Modeling

Intermittency analysis provides a promising probe of scale-invariant density fluctuations near the QCD critical point. The intermittency measurements reported in the STAR BES-I data call for a quantitative assessment of the signal strength and a clearer physical understanding of its collision-energy dependence. In this work, we perform such a study for the STAR measurements using an improved hybrid UrQMD+CMC model, in which critical-like fluctuations are embedded into a realistic non-critical background through event-level, particle-level, and combined replacement schemes. By directly comparing the second-order factorial moment $ΔF_{2}(M)$ between model calculations and experimental data on a point-by-point basis, we constrain the effective critical-like contribution compatible with the STAR measurements without relying on scaling exponents. The STAR data at $\sqrt{s_{\mathrm{NN}}}=7.7$--$27~\mathrm{GeV}$ used for model comparison can be consistently described only by small and nearly energy-independent effective critical-like fractions. These results indicate that the current BES-I intermittency signal is weak and exhibits little collision-energy dependence, thereby favoring only a limited critical-like contribution rather than a strong critical-point-induced enhancement localized near a specific collision energy.

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arXiv2026

Model Study of Eigen-Microstate Signatures of Criticality in Relativistic Heavy-Ion Collisions

We present a comprehensive model study of the eigen-microstate approach (EMA) for identifying critical fluctuations in relativistic heavy-ion collisions. Using UrQMD and two stochastic baseline models, we demonstrate that EMA is insensitive to conventional short-range correlations and effectively filters out non-critical backgrounds. Critical fluctuations embedded via event-level or particle-level replacement with CMC events generate characteristic cluster-like eigen-microstate patterns and enhanced leading eigenvalues, with event-level criticality producing stronger responses. The eigen microstates exhibit the same pattern across different scales, demonstrating that the fractal nature of critical fluctuations is captured by the eigen microstates. Finite-size scaling of eigenvalue ratios exhibits fixed-point behavior, confirming the largest eigenvalue as an effective order-parameter-like quantity. These results demonstrate that EMA offers a robust and background-independent method for critical-point searches in the RHIC Beam Energy Scan and future heavy-ion experiments.

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arXiv2025

Eigen-microstate Signatures of Criticality in Relativistic Heavy-Ion Collisions

We develop the eigen-microstate framework as a new approach to identify criticality in relativistic heavy-ion collisions. We construct the original microstate, defined as the final-state particle fluctuations of a single event. By examining ensembles of such original microstates with and without critical signals, we demonstrate that the corresponding eigen-microstate can extract and reveal the dominant critical mode, with the largest eigenvalue serving as a robust order parameter. This framework avoids equilibrium assumptions and is insensitive to non-critical background, and the approach is directly applicable to RHIC Beam Energy Scan data, offering a powerful new tool in the search for the QCD critical point.

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arXiv2025

CGC-induced longitudinal ridge in p-Pb collisions

Within the Color Glass Condensate (CGC) effective field theory, we investigate the long-range rapidity correlations in proton-lead (p-Pb) collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV. A distinctive correlation rebound is observed, where the correlation bounces after reaching a minimum at large rapidity gaps ($|Δη|>2$). The rebound means a strong correlation appears at large rapidity gap. Studying the rebound structures can thus illuminate the formation of the ridge. We find that the rebound is most obvious when the transverse momenta of two measured particles are around 2 $\mathrm{GeV/c}$, and it moves to larger rapidity gaps at higher collision energies. Beyond that, the rapidity correlations in p-Pb collisions show asymmetry when the transverse momenta of two particles differ. The asymmetry, a unique signature of the asymmetric collisions, vanishes when the transverse momenta of two particles coincide. These findings provide direct insight into gluon saturation and quantum evolution.

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arXiv2025

Relaxation dynamics and the free energy near the phase boundary of the 3D kinetic Ising model

We investigate relaxation dynamics along the entire first-order phase transition line by analyzing the time evolution of the free energy landscape in the three-dimensional kinetic Ising model. Near the critical temperature $T_{\rm c}$, the free energy structure is consistent with predictions from Landau-Ginzburg theory. At temperatures far below $T_{\rm c}$, however, fine structures in pre-equilibrium configurations trap random initial states, causing a pronounced delay in equilibration - an effect we identify as ultra-slow relaxation. This phenomenon is characterized by a self-divergence of the relative variance of equilibration times, which we propose as a previously unrecognized hallmark of first-order phase transitions.

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arXiv2025

Identifying weak critical fluctuations of intermittency in heavy-ion collisions with topological machine learning

Large density fluctuations of conserved charges have been proposed as a promising signature for exploring the QCD critical point in heavy-ion collisions. These fluctuations are expected to exhibit a fractal or scale-invariant behavior, which can be probed by intermittency analysis. Recent high-energy experimental studies reveal that the signal of critical fluctuations related to intermittency is very weak and thus could be easily obscured by the overwhelming background particles in the data sample. Employing a point cloud neural network with topological machine learning, we can successfully classify weak signal events from background noise by the extracted distinct topological features, and accurately determine the intermittency index for weak signal event samples.

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arXiv2025

Exploring percolation phase transition in the three-dimensional Ising model with machine learning

The percolation study offers valuable insights into the characteristics of phase transition, shedding light on the underlying mechanisms that govern the formation of global connectivity within the system. We explore the percolation phase transition in the 3D cubic Ising model by employing two machine learning techniques. Our results demonstrate the capability of machine learning methods in distinguishing different phases during the percolation transition. Through the finite-size scaling analysis on the output of the neural networks, the percolation temperature and a correlation length exponent in the geometrical percolation transition are extracted and compared to those in the thermal magnetization phase transition within the 3D Ising model. These findings provide a valuable way essential for enhancing our understanding of the property of the QCD critical point, which belongs to the same universality class as the 3D Ising model.

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arXiv2025

Relaxation behavior near the first-order phase transition line

Using the Metropolis algorithm, we simulate the relaxation process of the three-dimensional kinetic Ising model. Starting from a random initial configuration, we first present the average equilibration time across the entire phase boundary. It is observed that the average equilibration time increases significantly as the temperature decreases far from the critical temperature $T_{\rm c}$. The average equilibration time along the first-order phase transition (1st-PT) line exhibits an ultra-slow relaxation. We also investigate the dynamic scaling behavior with system sizes, and find that dynamic scaling holds not only near $T_{\rm c}$, but also at $T\ll T_{\rm c}$. The dynamic exponent at $T\ll T_{\rm c}$ is larger than that near $T_{\rm c}$. Additionally, we analyze the dynamic scaling of the average autocorrelation time and find that it depends on system size only near $T_{\rm c}$, while it becomes size-independent both above and below $T_{\rm c}$. The extremely slow relaxation dynamics observed near the 1st-PT is attributed to the complex structure of the free energy.

cond-mat.stat-mech↗

arXiv2025

Several problems on the measured hyperorder cumulants of net-proton distributions in heavy-ion collisions

Hyperorder cumulants $C_5/C_1$ and $C_6/C_2$ are recommended as sensitive observables to explore the QCD phase transition in heavy-ion collisions. Precisely measuring their results remains a difficult task in experiments, when employing the Centrality Bin Width Correction (CBWC) to suppress the initial volume fluctuations. Various techniques within the CBWC formula can lead to notable differences in the results. We will systematically investigate the application of the CBWC method to the measured net-proton $C_5/C_1$ and $C_6/C_2$ using the UrQMD model and Skellam-based simulations at $\sqrt{s_{NN}}$ = 11.5 GeV in Au + Au collisions. A recommended approach is proposed to calculate $C_5/C_1$ and $C_6/C_2$ in 0-40\% centrality. With statistics comparable to the RHIC Beam Energy Scan phase II (BES-II), our studies provide a baseline for analyzing net-proton $C_5/C_1$ and $C_6/C_2$ in relativistic heavy-ion collisions.

hep-ph↗

arXiv2024

The nonequilibrium evolution near the phase boundary

Using the single-spin flipping dynamics, we study the nonequilibrium evolution near the entire phase boundary of the 3D Ising model, and find that the average of relaxation time (RT) near the first-order phase transition line (1st-PTL) is significantly larger than that near the critical point (CP). As the system size increases, the average of RT near the 1st-PTL increases at a higher power compared to that near the CP. We further show that RT near the 1st-PTL is not only non-self-averaging, but actually self-diverging: relative variance of RT increases with system size. The presence of coexisting and metastable states results in a substantial increase in randomness near the 1st-PTL, and therefore makes the equilibrium more difficult to achieve.

cond-mat.stat-mech↗

arXiv2024

Impact of Limited Statistics on the Measured Hyper-Order Cumulants of Net-Proton Distributions in Heavy-Ion Collisions

Hyper-order cumulants $C_5/C_1$ and $C_6/C_2$ of net-baryon distributions are anticipated to offer crucial insights into the phase transition from quark-gluon plasma to hadronic matter in heavy-ion collisions. However, the accuracy of $C_5$ and $C_6$ is highly contingent on the fine shape of the distribution's tail, the detectable range of which could be essentially truncated by low statistics. In this paper, we use the fast Skellam-based simulations, as well as the Ultrarelativistic Quantum Molecular Dynamics model, to assess the impact of limited statistics on the measurements of $C_5/C_1$ and $C_6/C_2$ of net-proton distributions at lower RHIC energies. Both ratios decrease from the unity baseline as we reduce statistics, and could even turn negative without a pertinent physics mechanism. By incorporating statistics akin to experimental data, we can replicate the net-proton $C_5/C_1$ and $C_6/C_2$ values comparable to the corresponding measurements for Au+Au collisions at $\sqrt{s_{NN}} =$ 7.7, 11.5 and 14.5 GeV. Our findings underscore a caveat to the interpretation of the observed beam energy dependence of hyper-order cumulants.

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arXiv2023

Machine learning phase transitions of the three-dimensional Ising universality class

Exploration of the QCD phase diagram and critical point is one of the main goals in current relativistic heavy-ion collisions. The QCD critical point is expected to belong to a three-dimensional (3D) Ising universality class. Machine learning techniques are found to be powerful in distinguishing different phases of matter and provide a new way to study the phase diagram. We investigate phase transitions in the 3D cubic Ising model using supervised learning methods. It is found that a 3D convolutional neural network can be trained to effectivelly predict physical quantities in different spin configurations. With a uniform neural network architecture, it can encode phases of matter and identify both second- and first-order phase transitions. The important features that discriminate different phases in the classification processes are investigated. These findings can help study and understand QCD phase transitions in relativistic heavy-ion collisions.

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arXiv2022

Intermittency of charged particles in the hybrid UrQMD+CMC model at energies available at the BNL Relativistic Heavy Ion Collider

Within the framework of intermittency analysis, a search for critical fluctuations is ongoing to locate the possible critical point in the quantum chromodynamics phase diagram. In this study, self-similar critical fluctuations from a critical Monte Carlo (CMC) model have been incorporated into the cascade ultrarelativistic quantum molecular dynamics (UrQMD) model. This hybrid UrQMD+CMC model exhibits a clear power-law behavior of scaled factorial moment for charged particles in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 7.7-200 GeV. By comparing the UrQMD+CMC model results with those from the STAR experiment, it is found that the value of a calculated scaling exponent falls in the range of the experimental measurement when 1-2 \% signal of intermittency fluctuations is added into the UrQMD sample.

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arXiv2022

Large-rapidity ridge correlations from Color Glass Condensate

Within the Color Glass Condensate (CGC) effective field theory, considering the violation of boost invariance of the rapidity distribution, we correct the normalization scheme of the longitudinal rapidity ridge correlations. After this correction, the large-rapidity ridge correlation rebounds after bottoming, consistent with the observed data from the CMS detector. It is also found that the correlation rebound appears around the sum of the saturation momentum of the projectile and target, and moves to larger rapidities at higher collision energies. These features directly result from the saturation and the quantum evolution of gluons within the framework of the CGC.

hep-ph↗

arXiv2022

Investigations into the characteristics and influences of nonequilibrium evolution

In order to estimate qualitatively the influence of nonequilibrium evolution in relativistic heavy ion collisions, we use the three dimensional Ising model with Metropolis algorithm to study the evolution from nonequilibrium to equilibrium on the phase boundary. The evolution of order parameter approaches its equilibrium value exponentially, the same as that given by Langevin equation. The average relaxation time is defined which is demonstrated to well represent the relaxation time in dynamical equations. It is shown that the average relaxation time at critical temperature diverges as the zth power of system size. The third and the fourth cumulants of order parameter during the nonequilibrium evolution could be either positive or negative, depending on the observation time, consistent with dynamical models at T > Tc. It is found that the nonequilibrium evolution at T > Tc lasts very short, and the influence is weaker than that at T < Tc. Those qualitative features are instructive to determine experimentally the critical point and the phase boundary of QCD.

cond-mat.stat-mech↗

arXiv2021

Intermittency analysis of proton numbers in heavy-ion collisions at energies available at the BNL Relativistic Heavy Ion Collider

Local density fluctuations near the QCD critical point has been suggested to exhibit a power-law behavior which can be probed by an intermittency analysis on scaled factorial moment (SFM) in relativistic heavy-ion collisions. The collision energy and centrality dependence of the second-order SFMs are systematically investigated in Au $+$ Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 GeV within the UrQMD model. We estimate the noncritical background in the measurement of intermittency and propose a cumulative variable method to effectively remove the background contributions. We further study the effect of particle detection efficiency by implementing the RHIC (STAR) experimental tracking efficiencies in the UrQMD events. A cell-by-cell method is proposed for experimental application of efficiency corrections on SFM. This work can provide a guidance of background subtraction and efficiency correction for the experimental measurement of intermittency in the search of the QCD critical point in heavy-ion collisions.

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arXiv2021

Critical properties of various sizes of cluster in the Ising percolation transition

It is proposed that the $O(n)$ spin and geometrical percolation models can help to study the QCD phase diagram due to the universality properties of the phase transition. In this paper, correlations and fluctuations of various sizes of cluster in the Ising model are systematically studied. With a finite size system, we demonstrate how to use the finite size scaling and fixed point behavior to search for critical point. At critical point, the independency of system size is found from skewness and kurtosis of the maximum, second and third largest cluster and their correlations. It is similar to the Binder-ratio, which has provided a remarkable identification of the critical point. Through an explanation of the universal characteristic of skewness and kurtosis of the order parameter, a possible application to the relativistic heavy-ion collisions is also discussed.

hep-ph↗

arXiv2021

Measurement methods of radial flow in relativistic heavy-ion collisions

Radial flow can be directly extracted from the azimuthal distribution of mean transverse rapidity. We apply the event-plane method and the two-particle correlation method to estimate the anisotropic Fourier coefficient of the azimuthal distribution of mean transverse rapidity. Using the event sample generated by a multiphase transport model with string melting, we show that both methods are effective. For the two-particle correlation method to be reliable, the mean number of particles in an azimuthal bin must be above a certain threshold. Using these two methods, anisotropic radial flow can be estimated in a model-independent way in relativistic heavy-ion collisions.

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