arXiv subjects

Long-Gang Pang

Publications and source records attributed to Long-Gang Pang.

At least 19 recordsLinked to original sources

arXiv2026

Bottomonium suppression with a machine-learning-informed Debye mass

Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex-valued heavy-quark Kent State University (KSU) potential. The resulting complex potential is used to solve the real-time Schr\"odinger equation within the quantum trajectories (QTraj) framework for the evolution of bottomonium in the quark-gluon plasma. We then compute the nuclear modification factors and double ratios for bottomonium $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ states in Pb-Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV. We compare our ML-induced results with those from the original KSU model and with experimental measurements from ALICE, ATLAS, and CMS collaborations. We find that the machine-learned Debye mass leads to improved agreement with data, particularly for excited states, highlighting the utility of machine learning in modeling in-medium QCD effects.

hep-ph↗

arXiv2026

Hadronic rescattering effects on net-proton cumulants from functional renormalization group calculations

Net-proton cumulants in the Beam Energy Scan region of heavy-ion collisions are widely used to probe critical fluctuations associated with the conjectured critical endpoint of Quantum Chromodynamics (QCD). Most existing studies, however, concentrate on the initial-state or phase-transition contributions, while the impact of hadronic rescattering on these observables has not been fully quantified. To address this gap, we construct event-by-event proton and antiproton distributions from functional renormalization group (fRG) cumulants using the maximum entropy principle, and propagate the resulting particles through the hadronic transport model SMASH in a simplified spherical evolution setup. We systematically investigate how the hadronic cascade modifies net-proton cumulants at collision energies $\sqrt{s_{NN}}=3.0$, 3.9, 4.9, 7.2, and 7.7~GeV. In the canonical-ensemble framework, which enforces exact net-baryon number conservation, the higher-order cumulant signal---in particular the ratio $C_4/C_2$ at $\sqrt{s_{NN}}=4.9$~GeV---is strongly reduced during the early stage of the cascade; the suppression of $C_4/C_2$ reaches approximately $20\%$. The non-monotonic energy dependence inherited from the fRG input survives the hadronic evolution, but its magnitude is substantially modified. These results demonstrate that hadronic rescattering provides a non-negligible background effect that must be accounted for when extracting QCD critical-point signals from experimental data.

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arXiv2026

Neural network maximum entropy framework for distribution reconstruction in heavy-ion collisions

We develop a neural-network maximum-entropy (NN+MaxEnt) framework for reconstructing probability distributions from limited observables in heavy-ion collisions. The method combines flexible neural-network representations with Shannon-entropy regularization, preserving positivity and normalization without assuming a fixed analytic form. After validation with Gaussian, Poisson, and mixed-Poisson closure tests, we apply the framework to two physics-motivated inverse problems: an effective multiplicity reconstruction constrained by functional renormalization group cumulants, used as a closure test, and the conditional jet-energy-loss distribution extracted from single-inclusive jet $R_{AA}$ data in Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$~TeV. For the fRG closure test, NN+MaxEnt accurately reproduces the imposed cumulants and yields distributions consistent with conventional MaxEnt solutions. For jets, the reconstructed energy-loss distributions reproduce the measured $R_{AA}$; at an initial jet momentum $x=50~\mathrm{GeV}$, the conditional mean energy loss is $\langle\Delta p_T\rangle\simeq11.8~\mathrm{GeV}$, with a central $16\text{--}84\%$ interval of $9.0\text{--}15.0~\mathrm{GeV}$. The extracted energy-loss profile is qualitatively consistent with Bayesian MCMC and LBT results. NN+MaxEnt thus provides a flexible, less ansatz-dependent framework for regularized distribution reconstruction from observables connected to the underlying distribution through differentiable forward maps.

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arXiv2026

Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate

Gluon saturation limits the growth of parton densities at small Bjorken-$x$ and is expected to be most pronounced in heavy nuclei. Yet quantitative extractions of the nuclear gluon dipole amplitude have long relied on parametrized initial conditions, introducing uncontrolled model dependence that obscures genuine nuclear effects. We introduce a physics-informed neural-network framework that embeds the collinearly improved Balitsky-Kovchegov evolution equation directly into the training objective, allowing the impact-parameter-averaged dipole amplitude to be determined from data without assuming a functional form for its initial condition. Applying this framework to forward-hadron nuclear-modification-factor and coherent $J/\psi$ photoproduction data, we extract the $^{208}$Pb dipole amplitude at $x_0=0.01$ with QCD evolution and momentum-space positivity enforced throughout training. The evolved amplitude reproduces the measured cross sections across the available kinematic range and yields a saturation-scale ratio $Q_{s0,\mathrm{Pb}}^2/Q_{s0,p}^2 = 3.17^{+0.17}_{-0.10}$, consistent with simple geometric scaling. The extracted Pb initial condition is well described by a McLerran-Venugopalan-type form, in contrast to the proton, reflecting the higher color-charge density of a large nucleus. Using the same amplitude, we predict the rapidity dependence of the transverse-momentum ratio in $pp$, $p$Pb, and Pb$p$ collisions, finding agreement with recent LHCb measurements at low multiplicity without any system-dependent parameters. This work provides the first unbiased, data-driven determination of nuclear structure in the saturation regime and establishes a general strategy for embedding nonlinear evolution equations into machine-learning extractions of dynamically constrained observables.

hep-ph↗

arXiv2026

CLVisc Agent for autonomous relativistic hydrodynamics studies

We enable large language model (LLM) agents to autonomously perform end-to-end hydrodynamic simulations of the quark-gluon plasma evolution and calculation of final hadron spectra in relativistic heavy-ion collisions. We design a meta skill that allows an agent to explore a project's source code, craft a specialized skill, and iteratively refine it. Applying this meta skill to the (3+1)D viscous hydrodynamic code CLVisc, the agent builds a CLVisc skill encoding its operational knowledge and then independently executes full scientific workflows: designing parameter scans, running simulations, comparing ensemble results, and producing publication-ready figures. Crucially, the agent draws on literature-informed heavy-ion physics to select physically meaningful observables and interpret outcomes without explicit instruction. We demonstrate the pipeline in two scenarios: temperature-dependent shear viscosity over entropy density $\eta/s$, and nuclear-structure effects in O+O collisions at $\sqrt{s_{\mathrm{NN}}} = 5.36$~TeV using four \textit{ab initio} descriptions of $^{16}$O. In both, the agent plans, executes, and analyzes autonomously, devising new initial-state observables to explain final observations and extract qualitative knowledge. The meta skill is agnostic to code versions and Monte Carlo generators, promising future multi-agent systems in high-energy nuclear physics.

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arXiv2026

Study the Longitudinal Entropy Deposition using d+Au Collision

Relativistic hydrodynamics successfully describes bulk observables in symmetric heavy-ion collisions, but struggles to reproduce charged-particle rapidity distributions in asymmetric systems such as d+Au collisions. To address this challenge, we introduce two key improvements to the initial-state modeling: sampling deuteron configurations from an ab initio wavefunction, and developing a new longitudinal entropy deposition model that incorporates a transverse entropy deposition coefficient $\beta$ and a rapidity loss term scaling with the number of binary collisions $n_{\rm BC}$. Using the (3+1)-dimensional viscous hydrodynamic model CLVisc coupled with the SMASH afterburner, we simulate d+Au collisions at $\sqrt{s_{\rm NN}} = 200$ GeV and successfully reproduce the experimental charged-particle pseudorapidity distributions across five centrality classes with $\beta = 0.35$, as well as the transverse momentum spectra and anisotropic flow $v_n$. The entropy deposition coefficient $\beta$ and the $n_{\rm BC}$-dependent rapidity loss are found to play crucial roles in achieving this agreement. Furthermore, this longitudinal entropy deposition framework demonstrates excellent universality, as validated in p+Au, $^3$He+Au, and Au+Au collisions. Our entropy deposition mechanism could be widely applied to recent light-nucleus collisions such as O+O, Ne+Ne, and asymmetric systems like Pb+Ne at LHC energies, thereby better constraining the nuclear structure of light nuclei through an improved longitudinal description.

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arXiv2026

Physics-Informed Neural Network with Squeeze-Excitation-like Attention

We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the importance of neurons across layers. A key feature of SEA-PINN is its highly stable initialization. On 17 out of 20 benchmark problems, SEA-PINN exhibit nearly negligible variance and significantly reduced initial loss, establishing a quasi-deterministic and favorable starting point for optimization. Notably, without employing Fourier feature embeddings or periodic activation functions, SEA-PINN attained competitive accuracy (83\% vs. 90\% improvement relative to FNN-PINN on the high-frequency case 7) as compared with TSA-PINN-a model specifically engineered for high-frequency problems via learnable frequencies in sinusoidal activations. Furthermore, integrating SEA-PINN into TSA-PINN boosted performance by 42.49\%. These results underscore SEA-PINN as a lightweight plug-in module that enhances nonlinear representation power, promotes more robust and efficient convergence, and strengthens the overall reliability of physics-informed learning.

cs.LG↗

arXiv2026

Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model

In high-energy heavy-ion collisions, propagation of the energy deposited into the medium by energetic partons that traverse the quark-gluon plasma (QGP) leads to Mach-cone-like jet-induced medium response. Event-by-event simulations of jet-induced medium responses within a complete model such as the coupled Linear Boltzmann Transport and hydrodynamic (CoLBT-hydro) model are very resource-intensive. In this study, we develop a flow matching generative model trained by CoLBT-hydro events for the study of the medium response induced by $\gamma$-jets in high-energy heavy-ion collisions. With only the initial spatial and momentum information of the $\gamma$ and jets, the generative model is shown to conditionally reproduce the marginal final-state hadron spectra from the jet-induced hydro response in $0-10\%$ Pb+Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02~TeV. The generative model achieves a computational acceleration of approximately six orders of magnitude compared to the full CoLBT-hydro simulations, while faithfully preserving the statistical properties of the front and the diffusion wake of the Mach-cone-like hydro response and their contributions to the hadron spectra. Hadron spectra from the medium response, correlations between the front and diffusion wake and rapidity asymmetry due to the diffusion wake in $\gamma$-hadron correlation are further studied within the generative model.

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arXiv2026

Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks

The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simulations of relativistic heavy-ion collisions. We construct a four-dimensional EoS using a deep-learning-assisted quasi-particle model (DLQPM) within a physics-informed neural network (PINN) framework, in which the masses of light quarks, strange quarks, and gluons are parameterized as functions of temperature and chemical potentials ($T, \mu_B, \mu_Q, \mu_S$). The model is constrained by lattice QCD data at vanishing chemical potentials and provides a thermodynamically consistent extrapolation to finite $\mu_{B,Q,S}$. The DLQPM accurately reproduces the lattice-calculated cumulants $\chi^{B,Q,S}_{i,j,k}$ at $\mu_{B,Q,S}=0$, and its predicted EoS at various chemical potentials agrees well with results from the generalized $T'$-expansion method in lattice QCD. Furthermore, the calculated baryon-strangeness correlation $C_{BS}$ is consistent, within uncertainties, with preliminary STAR data. This work offers a reliable EoS for exploring the QCD phase structure in the beam energy scan region.

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arXiv2026

Global polarization of $\Lambda$ hyperons and its sensitivity to equations of state in low-energy heavy-ion collisions

Significant global polarization of $\Lambda$ hyperons along the direction of the orbital angular momentum has been measured in non-central heavy-ion collisions where the equation of state (EOS) of the produced dense matter is expected to change from intermediate to low colliding energies. We study the sensitivity of the global $\Lambda$ polarization to EOS in heavy-ion collisions within the SMASH transport model. Among the three different EOS we considered, only the hadron resonance gas (HRG) describes the experimental data well at low colliding energies even when it is below the $\Lambda$ production threshold in nucleon-nucleon collisions. The polarization induced by thermal vorticity as a function of centrality, rapidity, and transverse momentum at $\sqrt{s_{NN}} = 3$ GeV in Au+Au collisions is shown to agree well with the experimental data. Our study also indicates a possible peak in the global $\Lambda$ polarization around $\sqrt{s_{NN}} = 2.4$ GeV in Au+Au collisions. Furthermore, we find that the rapidity and transverse momentum-dependent helicity polarization induced by thermal vorticity vanishes due to space-reversal symmetry.

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arXiv2026

Physics-Informed Global Extraction of the Universal Small-$x$ Dipole Amplitude

We extract the universal small-$x$ dipole scattering amplitude $N(r,x_B)$ from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-type parametrization of the initial condition. The network provides a smooth and differentiable surrogate for $N(r,x_B)$, whose rapidity dependence is constrained by the collinearly improved Balitsky--Kovchegov evolution equation, while its functional form is simultaneously constrained by Deep Inelastic Scattering (DIS) data for the reduced total and charm cross sections, exclusive $J/\psi$ photoproduction measurements, and a positivity requirement for the momentum-space dipole amplitude. The resulting single universal amplitude consistently describes all fitted observables within a unified framework, alleviating the long-standing tension between total and charm channels encountered in conventional small-$x$ fits based on rigid parametric ans\"atze. Within the fitted kinematic domain, the best extracted PINN solution yields a smooth, non-negative momentum-space dipole over the full transverse-momentum range examined. Our results provide a robust and well-behaved input for Color Glass Condensate phenomenology across a broad class of high-energy processes.

hep-ph↗

arXiv2026

Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network

Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental data. Conventional methods often require parameterized forms of FFs and additional scale evolution according to the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations. We introduce a novel approach to determining parton FFs using a Physics-Informed Neural Network (PINN). Unlike traditional methods, our approach does not require prior parameterized forms and directly integrates the DGLAP evolution equations into the neural network architecture, allowing the FFs to automatically satisfy these equations. We present new sets of parton FFs extracted from hadron spectra in electron-positron annihilation processes at next-to-leading order (NLO) in pQCD using this new technique. To validate our approach, we calculate charged hadron spectra in proton-(anti)proton collisions using the extracted FFs and demonstrate that the results align well with experimental data across a large range of colliding energies ($\sqrt{s}$ = 130, 200, 500, 630, 900, 1800, 2760, 5020, 5440, 7000 GeV). Our findings indicate that the PINN method not only simplifies the extraction process but also enhances the universal applicability of FFs across different energy scales. By eliminating the need for parameterized forms and additional DGLAP evolution, our approach represents a significant step forward toward fast and accurate extractions of non-perturbative quantities such as parton fragmentations functions and parton distribution functions.

hep-ph↗

arXiv2025

Phase-space entropy at acquisition reflects downstream learnability

Modern learning systems work with data that vary widely across domains, but they all ultimately depend on how much structure is already present in the measurements before any model is trained. This raises a basic question: is there a general, modality-agnostic way to quantify how acquisition itself preserves or destroys the information that downstream learners could use? Here we propose an acquisition-level scalar $\Delta S_{\mathcal B}$ based on instrument-resolved phase space. Unlike pixelwise distortion or purely spectral errors that often saturate under aggressive undersampling, $\Delta S_{\mathcal B}$ directly quantifies how acquisition mixes or removes joint space--frequency structure at the instrument scale. We show theoretically that $\Delta S_{\mathcal B}$ correctly identifies the phase-space coherence of periodic sampling as the physical source of aliasing, recovering classical sampling-theorem consequences. Empirically, across masked image classification, accelerated MRI, and massive MIMO (including over-the-air measurements), $|\Delta S_{\mathcal B}|$ consistently ranks sampling geometries and predicts downstream reconstruction/recognition difficulty \emph{without training}. In particular, minimizing $|\Delta S_{\mathcal B}|$ enables zero-training selection of variable-density MRI mask parameters that matches designs tuned by conventional pre-reconstruction criteria. These results suggest that phase-space entropy at acquisition reflects downstream learnability, enabling pre-training selection of candidate sampling policies and as a shared notion of information preservation across modalities.

cs.LG↗

arXiv2025

Probing Neutron Skin through Event-by-Event Pion Asymmetry in Heavy-ion collisions

In this work, we propose a novel approach for probing the neutron skin thickness of gold (Au) by analyzing the event-by-event distribution of $\pi^{-}$ and $\pi^{+}$ yield differences. This is achieved through SMASH simulations of ultra-peripheral Au+Au collisions at $\sqrt{s_{\rm NN}}=3$ GeV. Our results demonstrate that the mean value of $\Delta n_{\pi} = n_{\pi^{-}} - n_{\pi^{+}}$, along with the Pearson correlation and mutual information between $(\pi^{-}+\pi^{+})$ and $(\pi^{-}-\pi^{+})$, all scale linearly with the neutron skin thickness. Moreover, the slope of the line connecting two distinct $\Delta n_{\pi}$ values in the event-by-event distribution also exhibits a linear dependence on the neutron skin thickness. The most sensitive $\Delta n_{\pi}$ pairs are identified as $(-1, 1)$, $(-1, 2)$, $(0, 1)$, and $(0, 2)$. These findings establish a new pathway for determining the neutron skin thickness. Finally, by comparing SMASH and UrQMD simulations under identical initial conditions, we observe that individual slope values depend on the specific collision model. However, by extracting slopes from multiple $\Delta n_{\pi}$ pairs in experimental event-by-event data and inferring the corresponding neutron skin thickness, one can assess which model better aligns with the true physical value.

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arXiv2025

Melting of heavy quarkonia in QGP using deep neural networks

Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the temperature-dependent behavior of the screening mass $m_D(T)$ and the strong coupling constant $\alpha_s(T)$ within a quark-gluon plasma medium. These medium-sensitive quantities are subsequently employed to compute the thermal widths $\Gamma_{\text{n}}(T)$ and binding energies $E_B(T)$ of heavy quarkonia states, specifically charmonia and bottomonia, by numerically solving the Schr\"odinger equation with medium-modified heavy quark potentials. To estimate the dissociation temperatures $T_d$ of various quarkonia states, we employ two complementary dissociation criteria: the conventional one, where $2E_B(T_d) = \Gamma_{\text{n}}(T_d)$, and an additional lower bound criterion defined by $E_B(T_d) = 3T_d$. This dual-criterion approach provides a more constrained and physically motivated estimate of the temperature range over which quarkonia states dissolve in the QGP environment. Our machine learning-enhanced predictions show excellent agreement with available lattice QCD results, especially for the ground states $\Upsilon(1S)$ and $J/\psi$, and offer new perspectives on the sequential suppression pattern detected in relativistic heavy-ion collision experiments. Overall, this work advances the quantitative description of quarkonium suppression and demonstrates the prospect of modern machine learning methods to bridge theoretical predictions and experimental observations, thereby contributing significantly to QGP tomography.

hep-ph↗

arXiv2025

Impact of Initial-State Nuclear and Sub-Nucleon Structures on Ultra-Central Puzzle in Heavy Ion Collisions

Hydrodynamic models fail to describe the near-equal $v_2/v_3$ ratio observed in ultra-central heavy-ion collisions, despite their success in other centrality classes. This discrepancy stems from shear viscosity suppressing higher-order geometric eccentricities, resulting in underestimated $v_3$ when using the conventional QGP viscosity coefficient. We explore two initial-state modifications to resolve this puzzle: (1) enforcing a minimum nucleon separation distance to homogenize distributions, and (2) amplifying sub-nucleon structures to reduce initial eccentricity. Using TRENTo initial conditions and 3+1D viscous hydrodynamic model CLVisc, both approaches significantly lower geometric eccentricity, reduce required viscosity, and narrow the $v_2$-$v_3$ gap in ultra-central collisions. Our results implicate initial-state nuclear and sub-nucleon structures as critical factors in addressing this puzzle. Resolving it would advance nuclear structure studies and improve precision in extracting QGP transport coefficients (e.g., shear viscosity), bridging microscopic nuclear features to macroscopic quark-gluon plasma properties.

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arXiv2025

A Novel Deep Learning Method for Detecting Nucleon-Nucleon Correlations

This study investigates the impact of nucleon-nucleon correlations on heavy-ion collisions using the hadronic transport model SMASH in $\sqrt{s_{\rm NN}}=3$ GeV $^{197}{\rm Au}$+$^{197}{\rm Au}$ collisions. We developed an innovative Monte Carlo sampling method that incorporates both single-nucleon distributions and nucleon-nucleon correlations. By comparing three initial nuclear configurations - a standard Woods-Saxon distribution (un-corr), hard-sphere repulsion (step corr), and ab initio nucleon-nucleon correlations (nn-corr)- we revealed minimal differences in traditional observables except for ultra-central collisions. When distinguishing between un-corr and nn-corr configurations, conventional attention-based point cloud networks and multi-event mixing classifiers failed (accuracy ~50%). To resolve this, we developed a novel deep learning architecture integrating multi-event statistics and high-dimensional latent space feature correlations, achieving 60\% overall classification accuracy, which improved to 70\% for central collisions. This method enables the extraction of subtle nuclear structure signals through statistical analysis in high-dimensional latent space, offering a new paradigm for studying initial-state nuclear properties and quark-gluon plasma characteristics in heavy-ion collisions. It overcomes the limitations of traditional single-event analysis in detecting subtle initial-state differences.

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arXiv2025

Effects of Initial Nucleon-Nucleon Correlations on Light Nuclei Production in Au+Au Collisions at $\sqrt{s_\mathrm{NN}} = 3\ $ GeV

Light nuclei production in heavy-ion collisions serves as a sensitive probe of the QCD phase structure. In coalescence models, triton ($N_t$) and deuteron ($N_d$) yields depend on the spatial separation of nucleon pairs ($\Delta r$) in Wigner functions, yet the impact of initial two-nucleon correlations $\rho(\Delta r)$ remains underexplored. We develop a method to sample nucleons in $^{197}$Au nuclei that simultaneously satisfies both the single-particle distribution $f(r)$ and the two-nucleon correlation $\rho(\Delta r)$. Using these nuclei, we simulate Au+Au collisions at $\sqrt{s_\mathrm{NN}}=3$ GeV via the SMASH transport model (mean-field mode) to calculate proton, deuteron, and triton yields. Simulations reveal a 36% enhancement in mid-rapidity deuteron yields across all centrality ranges and a 33% rise in mid-rapidity triton production for 0-10% central collisions. Calculated transverse momentum of light nuclei aligns with STAR data. We further analyze impacts of baryon conservation, spectator exclusion, and centrality determination via charged multiplicity. Notably, observed discrepancies in the double yield ratio suggest unaccounted physical mechanisms, such as critical fluctuations or inaccuracies in coalescence parameters or light nuclei cross-sections. This underscores the critical role of initial nucleon-nucleon correlations, linking microscopic nuclear structure to intermediate-energy collision dynamics.

hep-ph↗