arXiv subjects

Long-Gang Pang

Publications and source records attributed to Long-Gang Pang.

At least 37 records · Page 2Linked to original sources

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 ($Δr$) in Wigner functions, yet the impact of initial two-nucleon correlations $ρ(Δr)$ 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 $ρ(Δr)$. 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↗

arXiv2025

Nuclear equation of state at finite $μ_B$ using deep learning assisted quasi-parton model

To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential ($μ_B$) remains a challenging yet essential goal in the study of QCD matter under extreme conditions. In this study, we develop a deep learning assisted quasi-parton model, which utilizes three deep neural networks, to reconstruct the QCD EoS at zero $μ_B$ and predict the EoS and transport coefficient $η/s$ at finite $μ_B$. The EoS derived from our quasi-parton model shows excellent agreement with lattice QCD results obtained using Taylor expansion techniques. The minimum value of $η/s$ is found to be approximately 175 MeV and decreases with increasing chemical potential within the confidence interval. This model not only provides a robust framework for understanding the properties of the QCD EoS at finite $μ_B$ but also offers critical input for relativistic hydrodynamic simulations of nuclear matter produced in heavy-ion collisions by the RHIC beam energy scan program.

nucl-th↗

arXiv2025

On the uncertainty principle of neural networks

In this study, we explore the inherent trade-off between accuracy and robustness in neural networks, drawing an analogy to the uncertainty principle in quantum mechanics. We propose that neural networks are subject to an uncertainty relation, which manifests as a fundamental limitation in their ability to simultaneously achieve high accuracy and robustness against adversarial attacks. Through mathematical proofs and empirical evidence, we demonstrate that this trade-off is a natural consequence of the sharp boundaries formed between different class concepts during training. Our findings reveal that the complementarity principle, a cornerstone of quantum physics, applies to neural networks, imposing fundamental limits on their capabilities in simultaneous learning of conjugate features. Meanwhile, our work suggests that achieving human-level intelligence through a single network architecture or massive datasets alone may be inherently limited. Our work provides new insights into the theoretical foundations of neural network vulnerability and opens up avenues for designing more robust neural network architectures.

cs.LG↗

arXiv2024

Is AI Robust Enough for Scientific Research?

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas -- weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication -- we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security.

cs.LG↗

arXiv2024

Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion

Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles that govern these techniques remains elusive. Specifically, the role of symmetry breaking, a fundamental concept in physics, has not been fully explored in neural network optimization. This gap in knowledge limits our ability to design networks that are both efficient and effective. Here, we propose the symmetry breaking hypothesis to elucidate the significance of symmetry breaking in enhancing neural network optimization. We demonstrate that a simple input expansion can significantly improve network performance across various tasks, and we show that this improvement can be attributed to the underlying symmetry breaking mechanism. We further develop a metric to quantify the degree of symmetry breaking in neural networks, providing a practical approach to evaluate and guide network design. Our findings confirm that symmetry breaking is a fundamental principle that underpins various optimization techniques, including dropout, batch normalization, and equivariance. By quantifying the degree of symmetry breaking, our work offers a practical technique for performance enhancement and a metric to guide network design without the need for complete datasets and extensive training processes.

cs.LG↗

arXiv2024

Asymmetric jet shapes with 2D jet tomography

Two-dimensional (2D) jet tomography is a promising tool to study jet medium modification in high-energy heavy-ion collisions. It combines gradient (transverse) and longitudinal jet tomography for selection of events with localized initial jet production positions. It exploits the transverse asymmetry and energy loss that depend, respectively, on the transverse gradient and jet path length inside the quark-gluon plasma (QGP). In this study, we employ the 2D jet tomography to study medium modification of the jet shape of $γ$-triggered jets within the linear Boltzmann transport (LBT) model for jet propagation in heavy-ion collisions. Our results show that jets with small transverse asymmetry ($A_N^{\vec{n}}$) or small $γ$-jet asymmetry ($x_{Jγ}=p_T^{\rm jet}/p_T^γ$) exhibit a broader jet shape than those with larger $A_N^{\vec{n}}$ or $x_{Jγ}$, since the former are produced at the center and go through longer path lengths while the later are off-center and close to the surface of the QGP fireball. In events with finite values of $A_N^{\vec{n}}$, jet shapes are asymmetric with respect to the event plane. Hard partons at the core of the jet are deflected away from the denser region while soft partons from the medium response at large angles flow toward the denser part of QGP. Future experimental measurements of these asymmetric features of the jet shape can be used to study the transport properties of jets and medium responses.

hep-ph↗

arXiv2024

Phase Transition Study meets Machine Learning

In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.

nucl-th↗

arXiv2023

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.

hep-ph↗

arXiv2023

Solving Einstein equations using deep learning

Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric systems or ideal matter distribution. However, accurate solutions are crucial, particularly in systems with strong gravitational field such as black holes or neutron stars. In this work, we use neural networks and auto differentiation to solve the Einstein field equations numerically inspired by the idea of physics-informed neural networks (PINNs). By utilizing these techniques, we successfully obtain the Schwarzschild metric and the charged Schwarzschild metric given the energy-momentum tensor of matter. This innovative method could open up a different way for solving space-time coupled Einstein field equations and become an integral part of numerical relativity.

gr-qc↗

arXiv2023

High energy nuclear physics meets Machine Learning

Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results during the last few years. It's worthy to raise the profile of utilizing this novel mindset from machine learning in high energy nuclear physics, to help more interested readers see the breadth of activities around this intersection. The aim of this mini-review is to introduce to the community the current status and report an overview of applying machine learning for high energy nuclear physics, to present from different aspects and examples how scientific questions involved in high energy nuclear physics can be tackled using machine learning.

hep-ph↗

arXiv2023

Solving Schrodinger equations using physically constrained neural network

Deep neural network (DNN) and auto differentiation have been widely used in computational physics to solve variational problems. When DNN is used to represent the wave function to solve quantum many-body problems using variational optimization, various physical constraints have to be injected into the neural network by construction, to increase the data and learning efficiency. We build the unitary constraint to the variational wave function using a monotonic neural network to represent the Cumulative Distribution Function (CDF) $F(x) = \int_{-\infty}^{x} ψ^*ψdx'$. Using this constrained neural network to represent the variational wave function, we solve Schrodinger equations using auto-differentiation and stochastic gradient descent (SGD), by minimizing the violation of the trial wave function $ψ(x)$ to the Schrodinger equation. For several classical problems in quantum mechanics, we obtain their ground state wave function and energy with very low errors. The method developed in the present paper may pave a new way in solving nuclear many body problems in the future.

nucl-th↗

arXiv2023

3D structure of jet-induced diffusion wake in an expanding quark-gluon plasma

The diffusion wake accompanying the jet-induced Mach cone provides a unique probe of the properties of quark-gluon plasma in high-energy heavy-ion collisions. It can be characterized by a depletion of soft hadrons in the opposite direction of the propagating jet. We explore the 3D structure of the diffusion wake induced by $γ$-triggered jets in Pb+Pb collisions at the LHC energy within the coupled linear Boltzmann transport and hydro model. We identify a valley structure caused by the diffusion wake on top of a ridge from the initial multiple parton interaction (MPI) in jet-hadron correlation as a function of rapidity and azimuthal angle. This leads to a double-peak structure in the rapidity distribution of soft hadrons in the opposite direction of the jets as an unambiguous signal of the diffusion wake. Using a two-Gaussian fit, we extract the diffusion wake and MPI contributions to the double peak. The diffusion wake valley is found to deepen with the jet energy loss as characterized by the $γ$-jet asymmetry. Its sensitivity to the equation of state and shear viscosity is also studied.

hep-ph↗

arXiv2022

Deep-learning quasi-particle masses from QCD equation of state

The interactions of quarks and gluons are strong at non-perturbative region. The equation of state (EoS) of a strongly-interacting quantum chromodynamics (QCD) medium can only be studied using the first-principle lattice QCD calculations. However, the complicated QCD EoS can be reproduced using simple statistical formula by treating the medium as a free parton gas whose fundamental degree of freedoms are dressed quarks and gluons called quasi-particles, with temperature-dependent masses. We use deep neural network and auto differentiation to solve this variational problem in which the masses of quasi gluons, up/down and strange quarks are three unknown functions, whose forms are represented by deep neural network. We reproduce the QCD EoS using these machine learned quasi-particle masses, and calculate the shear viscosity over entropy density ($η/s$) as a function of temperature of the hot QCD matter.

hep-ph↗

arXiv2022

Deep learning on nuclear mass and $α$ decay half-lives

Ab-initio calculations of nuclear masses, the binding energy and the $α$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and neutron number($\mathrm{Z}$) grow. We take advantage of the powerful non-linear transformation and feature representation ability of deep neural network(DNN) to predict the nuclear masses and $α$ decay half-lives. For nuclear binding energy prediction problem we achieve standard deviation $σ=0.263$ MeV on 10-fold cross validation on 2149 nuclei. Word-vectors which are high dimensional representation of nuclei from the hidden layers of mass-regression DNN help us to calculate $α$ decay half-lives. For this task, we get $σ=0.797$ on 100 times 10-fold cross validation on 350 nuclei on $log_{10}T_{1/2}$ and $σ=0.731 $ on 486 nuclei. We also find physical a priori such as shell structure, magic numbers and augmented inputs inspired by Finite Range Droplet Model are important for this small data regression task.

nucl-th↗

arXiv2022

Deep learning assisted jet tomography for the study of Mach cones in QGP

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons (called jets) traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the jet propagation direction relative to the radial flow because of the distortion by the collective expansion of the QGP and large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different jet path length and orientation relative to the radial flow of the QGP in heavy-ion collisions.

nucl-th↗

arXiv2022

Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

nucl-th↗

arXiv2022

(3+1)-D viscous hydrodynamics CLVisc at finite net baryon density: identified particle spectra, anisotropic flows and flow fluctuations across BES energies

To study the bulk properties of the quark-gluon-plasma (QGP) produced at the beam energy scan (BES) energies at the Relativistic Heavy Ion Collider (RHIC), we extend the (3+1)-dimensional viscous hydrodynamics CLVisc to include net baryon number conservation and Israel-Stewart-like equation for baryon diffusion with the NEOS-BQS equation of state, fluctuating initial conditions from Monte-Carlo Glauber model, and the afterburner SMASH. This integrated framework is shown to provide a good description of identified particle spectra, mean transverse momenta and anisotropic flows for different centralities and over a wide range of collision energies (7.7-62.4 GeV). It is found that the mean momenta of identified particles and anisotropic flows increases mildly with the collision energy due to larger radial flow. We further compute the multiple-particle cumulant ratio $v_2\{4\}/v_2\{2\}$ of elliptic flow across BES energies, and find that the relative fluctuations of elliptic flow are insensitive to the collision energy, consistent with the preliminary STAR data. Our model provides a benchmark for understanding the RHIC-BES data and studying the critical properties and phase structure of hot and dense QCD matter.

hep-ph↗

arXiv2022

Probing criticality with deep learning in relativistic heavy-ion collisions

Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.

nucl-th↗