arXiv subjects

Ranran Guo

Publications and source records attributed to Ranran Guo.

8 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 $\Delta F_{2}(M)$ 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.

hep-ph↗

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.

cond-mat.stat-mech↗

arXiv2024

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↗

arXiv2023

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.

nucl-th↗

arXiv2023

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↗

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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