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Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, matching the Andreev-Zakharov model exactly and confirming the validity of MLP. Finally, we construct an analytical holographic model using KAN and study the heavy-quark potential at finite temperature and chemical potential using the KAN-based holographic model. This work demonstrates the potential of KAN to derive analytical expressions for high-energy physics applications.

Based on the holographic model, which incorporates the equation of state (EoS) and baryon number susceptibility for different flavors, we calculate the drag force, jet quenching parameter, and diffusion coefficient of the heavy quark at finite temperature and chemical potential. The holographic results for the diffusion coefficient align with lattice data for $N_f = 0$ and $N_f = 2+1$, falling within their error margins. The holographic diffusion coefficient for heavy quark in the systems of different flavors agrees with the fitting results of ALICE data well. The jet quenching parameter in our model demonstrates strong consistency with the results from both RHIC and LHC for different flavors. By comparing the experimental data with other models, we can confirm the validity of our holographic model calculation. The work reinforces the potential of bottom-up holographic model in advancing our understanding of transport properties of hot and dense quark-gluon plasma.

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., Comment: arXiv admin note: text overlap with arXiv:2303.06752

Collective flow has been found to be similar between small colliding systems ($p$ $+$ $p$ and $p$ $+$ A collisions) and large colliding systems (peripheral A $+$ A collisions) at the CERN Large Hadron Collider. In order to study the differences of collective flow between small and large colliding systems, we employ a point cloud network to identify $p$ $+$ Pb collisions and peripheral Pb $+$ Pb collisions at $\sqrt{s_{NN}} =$ 5.02 TeV generated from a multiphase transport model (AMPT). After removing the discrepancies in the pseudorapidity distribution and the $p_{\rm T}$ spectra, we capture the discrepancy in collective flow. Although the verification accuracy of our PCN is limited due to similar event-by-event distributions of elliptic and triangular flow, we demonstrate that collective flow between $p$ $+$ Pb collisions and peripheral Pb $+$ Pb collisions becomes more distinct with increasing final hadron multiplicity and parton scattering cross section. This study not only highlights the potential of PCN techniques in advancing the understanding of collective flow in varying colliding systems, but more importantly lays the groundwork for the future PCN-related research., Comment: 10 pages, 11 figures, final published version

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., Comment: 152 pages,53 figures

The centrality determination and the estimated fluctuations of number of participant nucleons $N_{part}$ in Au-Au collisions at 1.23 $A$GeV beam kinetic energy suffers from severe model dependencies. Comparing the Glauber Monte Carlo (MC) and UrQMD transport models, it is shown that $N_{part}$ is a strongly model dependant quantity. In addition, for any given centrality class, Glauber MC and UrQMD predicts drastically different $N_{part}$ distributions. The impact parameter $b$ and the number of charged particles $N_{ch}$ on the other hand are much more correlated and give an almost model independent centrality estimator. It is suggested that the total baryon number balance, from integrated rapidity distributions, can be used instead of $N_{part}$ in experiments. Preliminary HADES data show significant differences to both, UrQMD simulations and STAR data in this respect., Comment: Replaced with the published version

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., Comment: 30 pages, 20 figures, mini-review

We study the momentum and centrality dependence of the non-prompt $J/\psi$ nuclear modification factors ($R_{AA}$), which comes from the $B$ hadrons decay, in Pb-Pb collisions at the Large Hadron Collider. Bottom quarks are produced in the parton hard scatterings and suffer energy loss in the quark-gluon plasma and the hadronic gas, where the spatial and time evolution of the medium is described with the hydrodynamic equations. Medium-induced elastic scatterings and the radiation in bottom quarks are included in the energy loss of bottom quarks. The hadronization process of bottom quarks is described with the instantaneous coalescence model. After considering both cold and hot nuclear matter effects in Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV, we calculated the $R_{AA}$ and also the elliptic flows of non-prompt $J/\psi$ from the decay of $B$ mesons at different centralities and transverse momentum bins. The $R_{AA}$ and $v_2$ of non-prompt $J/\psi$ sensitive to the hot medium reflect and centrality supply an opportunity to study the bottom quarks energy loss in the hot medium., Comment: 7 pages, 6 figures

Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon distributions for nuclei with the same mass number. In recent contrast experiment of $^{96}_{44}\text{Ru}+^{96}_{44}\text{Ru}$ versus $^{96}_{40}\text{Zr}+^{96}_{40}\text{Zr}$ collisions at $\sqrt{s_\text{NN}} = 200~\text{GeV}$, the ratios of multiplicity distribution, elliptic flow, triangular flow, and radial flow are precisely measured and found to be significantly different from unity, indicating the difference in the shapes of the isobar pair. In this work, we investigate the feasibility of nuclear structure reconstruction from heavy-ion collision observables. We perform Bayesian Inference with employing the Monte-Carlo Glauber model as an estimator of the mapping from nuclear structure to the final state observables and to provide the mock data for reconstruction. By varying combination of observables included in the mock data, we find it plausible to infer Woods--Saxon parameters from the observables. We also observe that single-system multiplicity distribution for the isobar system, rather than their ratio, is crucial to simultaneously determine the nuclear structure for the isobar system., Comment: Previously entitled "How does Bayesian analysis infer the nucleon distributions in isobar collisions?"

Bayesian methods are used to constrain the density dependence of the QCD Equation of State (EoS) for dense nuclear matter using the data of mean transverse kinetic energy and elliptic flow of protons from heavy ion collisions (HIC), in the beam energy range $\sqrt{s_{\mathrm{NN}}}=2-10 GeV$. The analysis yields tight constraints on the density dependent EoS up to 4 times the nuclear saturation density. The extracted EoS yields good agreement with other observables measured in HIC experiments and constraints from astrophysical observations both of which were not used in the inference. The sensitivity of inference to the choice of observables is also discussed., Comment: Replaced with the published version

We present lattice QCD calculations of valence parton distribution function (PDF) of pion employing next-to-next-leading-order (NNLO) perturbative QCD matching. Our calculations are based on three gauge ensembles of 2+1 flavor highly improved staggered quarks and Wilson--Clover valance quarks, corresponding to pion mass $m_\pi=140$~MeV at a lattice spacing $a=0.076$~fm and $m_\pi=300$~MeV at $a=0.04, 0.06$~fm. This enables us to present, for the first time, continuum-extrapolated lattice QCD results for NNLO valence PDF of the pion at the physical point. Applying leading-twist expansion for renormalization group invariant (RGI) ratios of bi-local pion matrix elements with NNLO Wilson coefficients we extract $2^{\mathrm{nd}}$, $4^{\mathrm{th}}$ and $6^{\mathrm{th}}$ Mellin moments of the PDF. We reconstruct the Bjorken-$x$ dependence of the NNLO PDF from real-space RGI ratios using a deep neural network (DNN) as well as from momentum-space matrix elements renormalized using a hybrid-scheme. All our results are in broad agreement with the results of global fits to the experimental data carried out by the xFitter and JAM collaborations., Comment: 25 pages, 29 figures, version published in PRD

Reconstructing hadron spectral functions through Euclidean correlation functions are of the important missions in lattice QCD calculations. However, in a K\"allen--Lehmann(KL) spectral representation, the reconstruction is observed to be ill-posed in practice. It is usually ascribed to the fewer observation points compared to the number of points in the spectral function. In this paper, by solving the eigenvalue problem of continuous KL convolution, we show analytically that the ill-posedness of the inversion is fundamental and it exists even for continuous correlation functions. We discussed how to introduce regulators to alleviate the predicament, in which include the Artificial Neural Networks(ANNs) representations recently proposed by the Authors in~[Phys. Rev. D 106 (2022) L051502]. The uniqueness of solutions using ANNs representations is manifested analytically and validated numerically. Reconstructed spectral functions using different regularization schemes are also demonstrated, together with their eigen-mode decomposition. We observe that components with large eigenvalues can be reliably reconstructed by all methods, whereas those with low eigenvalues need to be constrained by regulators., Comment: 29 pages, 6 figures; final version. source code and data are available at: https://github.com/ShuzheShi/SpectralFunction}{https://github.com/ShuzheShi/SpectralFunction

The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in measurements of neutron star masses, radii, and tidal deformabilities, from electromagnetic and gravitational wave observations, neutron stars play an important role in constraining the ultra-dense QCD matter EoS. In this work, we present a novel method that exploits deep learning techniques to reconstruct the neutron star EoS from mass-radius (M-R) observations. We employ neural networks (NNs) to represent the EoS in a model-independent way, within the range of $\sim$1-7 times the nuclear saturation density. The unsupervised Automatic Differentiation (AD) framework is implemented to optimize the EoS, so as to yield through TOV equations, an M-R curve that best fits the observations. We demonstrate that this method works by rebuilding the EoS on mock data, i.e., mass-radius pairs derived from a randomly generated polytropic EoS. The reconstructed EoS fits the mock data with reasonable accuracy, using just 11 mock M-R pairs observations, close to the current number of actual observations., Comment: 24 pages, 14 figures, https://github.com/ss-fias/nn-eos

Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in many-body physics. However, the inversion is proved to be ill-posed in the realistic systems with noisy Green's functions. In this Letter, we propose an automatic differentiation(AD) framework as a generic tool for the spectral reconstruction from propagator observable. Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly. In the training process, except for the positive-definite form for the spectral function, there are no other explicit physical priors embedded into the neural networks. The reconstruction performance is assessed through relative entropy and mean square error for two different network representations. Compared to the maximum entropy method, the AD framework achieves better performance in the large-noise situation. It is noted that the freedom of introducing non-local regularization is an inherent advantage of the present framework and may lead to substantial improvements in solving inverse problems., Comment: 9 pages, 10 figures. Updated to the published version

A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0-7 fm). However, the performance is found to improve for central collisions (b=0-3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables., Comment: 12 pages, 7 figures. Matches published version

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms., Comment: 14 pages, 3 figures, 5 tables - Version accepted by Computing and Software for Big Science

We study the applicability of a Deep Neural Network (DNN) approach to simulate one-dimensional non-relativistic fluid dynamics. Numerical fluid dynamical calculations are used to generate training data-sets corresponding to a broad range of profiles to perform supervised learning with DNN. The performance of the DNN approach is analyzed, with a focus on its interpolation and extrapolation capabilities. Issues such as inference speed, the networks capacities to interpolate and extrapolate solutions with limited training samples from both initial geometries and evolution duration aspects are studied in detail. The optimal DNN performance is achieved when its objective is set to learn the mapping between hydro profiles after a fixed value time step, which can then be applied successively to reach moments in time much beyond the duration contained in the training. The DNN has an advantage over the conventional numerical methods by not being restricted by the Courant criterion, and it shows a speedup over the conventional numerical methods by at least two orders of magnitude., Comment: 12 pages, 11 figures

The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we construct an observable-independent CME-meter based on a deep convolutional neural network. After trained over data set generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In a transfer learning manner, the CME-meter is validated in isobaric collision systems, showing good transferability among different colliding systems. Based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectra that demonstrates the physical contents the machine learns., Comment: 7 pages, 10 figures

Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks. The latter can be characterized by the temperature ($T$) dependent potential, with real ($V_R(T,r)$) and imaginary ($V_I(T,r)$) parts, as a function of the spatial separation ($r$). Recently, the masses and thermal widths of up to $3S$ and $2P$ bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network, here, we obtain $V_R(T,r)$ and $V_I(T,r)$ in a model-independent fashion. The temperature dependence of $V_R(T,r)$ was found to be very mild between $T\approx0-334$~MeV. For $T=151-334$~MeV, $V_I(T,r)$ shows a rapid increase with $T$ and $r$, which is much larger than the perturbation-theory-based expectations., Comment: 16 pages, 10 figures; Published version; Data for plots enclosed

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade "after-burner". As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario., Comment: 4 pages, 4 figures, Quark Matter 2019, will be published in Nucl. Phys. A

A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9 % and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10 % and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units., Comment: Replaced with version accepted for publication. 10 pages, 8 figures

We present different methods of unsupervised learning which can be used for outlier detection in high energy nuclear collisions. The UrQMD model is used to generate the bulk background of events as well as different variants of outlier events which may result from misidentified centrality or detector malfunctions. The methods presented here can be generalized to different and novel physics effects. To detect the outliers, dimensional reduction algorithms are implemented, specifically the Principle Component Analysis (PCA) and Autoencoders (AEN). We find that mainly the reconstruction error is a good measure to distinguish outliers from background. The performance of the algorithms is compared using a ROC curve. It is shown that the number of reduced (encoded) dimensions to describe a single event contributes significantly to the performance of the outlier detection task. We find that the model which is best suited to separate outlier events requires a good performance in reconstructing events and at the same time a small number of parameters., Comment: 24 pages, 12 figures, new version with added references in section 1 and slightly improved layout of some figures

Heavy flavor hadrons have long been considered as a probe of the quark gluon plasma created in high energy nuclear collisions. In this paper we review the heavy flavor properties under extreme conditions and the realization in heavy ion experiments. After a short introduction on heavy flavor properties in vacuum, we emphasize the cold and hot nuclear matter effects on heavy flavors, including shadowing effect, Cronin effect and nuclear absorption for the former and Debye screening and regeneration for the latter. Then we discuss, in the frame of transport and coalescence models, these medium induced changes in open and closed heavy flavors in nuclear collisions and the comparison with nucleon-nucleon collisions. Considering the extremely strong electromagnetic and rotational fields generated in non-central nuclear collisions, which are widely studied in recent years, we finally investigate their effects on heavy flavor production and evolution in high energy nuclear collisions., Comment: 90 pages, 35 figures, accepted by Progress in Particle and Nuclear Physics

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade "after-burner". Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments., Comment: 16 pages, 21 figures. Updated version for the publication in Eur. Phys. J. C

The phenomenon of Bose-Einstein condensation is investigated in the context of the Color-Glass-Condensate description of the initial state of ultrarelativistic heavy-ion collisions. For the first time, in this paper we study the influence of particle-number changing $2 \leftrightarrow 3$ processes on the transient formation of a Bose-Einstein Condensate within an isotropic system of scalar bosons by including $2 \leftrightarrow 3$ interactions of massive bosons with constant and isotropic cross sections, following a Boltzmann equation. The one-particle distribution function is decomposed in a condensate part and a non-zero momentum part of excited modes, leading to coupled integro-differential equations for the time evolution of the condensate and phase-space distribution function, which are then solved numerically. Our simulations converge to the expected equilibrium state, and only for $\sigma_{23}/\sigma_{22} \ll 1$ we find that a Bose-Einstein condensate emerges and decays within a finite lifetime in contrast to the case where only binary scattering processes are taken into account, and the condensate is stable due to particle-number conservation. Our calculations demonstrate that Bose-Einstein Condensates in the very early stage of heavy-ion collisions are highly unlikely, if inelastic collisions are significantly participating in the dynamical gluonic evolution., Comment: 6 pages, 4 figures; v2: version as published

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differentiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such scenarios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy ($\mathrm{E}_{\mathrm{lab}}= 3-4$ A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition., Comment: 24 pages, 14 figures, accepted for publication by JHEP

The structure of heavy nuclei is difficult to disentangle in high-energy heavy-ion collisions. The deep convolution neural network (DCNN) might be helpful in mapping the complex final states of heavy-ion collisions to the nuclear structure in the initial state. Using DCNN for supervised regression, we successfully extracted the magnitude of the nuclear deformation from event-by-event correlation between the momentum anisotropy or elliptic flow ($v_2$) and total number of charged hadrons ($dN_{\rm ch}/d\eta$) within a Monte Carlo model. Furthermore, a degeneracy is found in the correlation between collisions of prolate-prolate and oblate-oblate nuclei. Using the Regression Attention Mask algorithm which is designed to interpret what has been learned by DCNN, we discovered that the correlation in total-overlapped collisions is sensitive to only large nuclear deformation, while the correlation in semi-overlapped collisions is discriminative for all magnitudes of nuclear deformation. The method developed in this study can pave a way for exploration of other aspects of nuclear structure in heavy-ion collisions., Comment: 8 pages, 2 figures, AI + X research

We explore the perspectives of machine learning techniques in the context of quantum field theories. In particular, we discuss two-dimensional complex scalar field theory at nonzero temperature and chemical potential -- a theory with a nontrivial phase diagram. A neural network is successfully trained to recognize the different phases of this system and to predict the value of various observables, based on the field configurations. We analyze a broad range of chemical potentials and find that the network is robust and able to recognize patterns far away from the point where it was trained. Aside from the regressive analysis, which belongs to supervised learning, an unsupervised generative network is proposed to produce new quantum field configurations that follow a specific distribution. An implicit local constraint fulfilled by the physical configurations was found to be automatically captured by our generative model. We elaborate on potential uses of such a generative approach for sampling outside the training region., Comment: 11 pages, 11 figures

Dilepton production from two photon interactions $\gamma\gamma\rightarrow l^+l^-$ are studied in semi-central and peripheral nuclear collisions. Based on Weizs\"acker-Williams approach, it is shown that the dilepton photoproduction is proportional to the electromagnetic (EM) fields $\sim E^2B^2$ and therefore sensitive to the magnitude and lifetime of initial EM fields which last only for a short time and are hard to be measured in experiments directly. We propose dilepton photoproduction as a probe for the nuclear charge fluctuations, which are crucial for the electric/magnetic field induced chiral and charge particle evolutions. We calculate the relative standard deviation of dilepton mass spectrum with event-by-event fluctuating nuclear charge distributions (and EM fields)., Comment: 4 pages, 4 figures

We present a kinetic description of Bose-Einstein condensation for particle systems being out of thermal equilibrium, which may happen for gluons produced in the early stage of ultra-relativistic heavy-ion collisions. The dynamics of bosons towards equilibrium is described by a Boltzmann equation including Bose factors. To solve the Boltzmann equation with the presence of a Bose-Einstein condensate we make further developments of the kinetic transport model BAMPS (Boltzmann Approach of MultiParton Scatterings). In this work we demonstrate the correct numerical implementations by comparing the final numerical results to the expected solutions at thermal equilibrium for systems with and without the presence of Bose-Einstein condensate. In addition, the onset of the condensation in an over-populated gluon system is studied in more details. We find that both expected power-law scalings denoted by the particle and energy cascade are observed in the calculated gluon distribution function at infrared and intermediate momentum regions, respectively. Also, the time evolution of the hard scale exhibits a power-law scaling in a time window, which indicates that the distribution function is approximately self-similar during that time., Comment: 15 pages,14 figures

We discuss the implementation of leading-order photon production in nonequilibrium partonic transport simulations. In this framework photons are produced by microscopic scatterings, where we include the exact matrix elements of Compton scattering, quark-antiquark annihilation, and bremsstrahlung processes. We show how the hard-thermal loop inspired screening of propagators has to be modified such that the microscopic production rate agrees well with the analytically known resummed leading-order rate. We model the complete quark-gluon plasma phase of heavy-ion collisions by using the partonic transport approach called the Boltzmann approach to multiparton scatterings (BAMPS), which solves the ultrarelativistic Boltzmann equation with Monte Carlo methods. We show photon spectra and elliptic flow of photons from BAMPS and discuss nonequilibrium effects. Due to the slow quark chemical equilibration in BAMPS, the yield is lower than the results from other groups; in turn we see a strong effect from scatterings of energetic jet-like partons with the medium. This nonequilibrium photon production can dominate the thermal emission, such that the spectra are harder and the photonic elliptic flow of the quark-gluon plasma becomes negative., Comment: 20 pages, 24 figures. Revised version as accepted by PRC

Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$. High-level correlations of $\rho(p_T,\Phi)$ learned by the neural network act as an effective "EoS-meter" in detecting the nature of the QCD transition. The EoS-meter is model independent and insensitive to other simulation inputs, especially the initial conditions. Thus it provides a powerful direct-connection of heavy-ion collision observables with the bulk properties of QCD.

We study the effect of thermal charm production on charmonium regeneration in high energy nuclear collisions. By solving the kinetic equations for charm quark and charmonium distributions in Pb+Pb collisions, we calculate the global and differential nuclear modification factors $R_{AA}(N_{part})$ and $R{AA}(p_t)$ for $J/\Psi$s. Due to the thermal charm production in hot medium, the charmonium production source changes from the initially created charm quarks at SPS, RHIC and LHC to the thermally produced charm quarks at Future Circular Collider (FCC), and the $J/\Psi$ suppression ($R_{AA}<1$) observed so far will be replaced by a strong enhancement ($R_{AA}>1$) at FCC at low transverse momentum., Comment: 6 pages, 3 figures

The strong interaction between heavy quarks and the quark gluon plasma makes the open and hidden charm hadrons be sensitive probes of the deconfinement phase transition in high energy nuclear collisions. Both the cold and hot nuclear matter effects change with the colliding energy and significantly influence the heavy quark and charmonium yield and their transverse momentum distributions. The ratio of averaged quarkonium transverse momentum square and the elliptic flow reveal the nature of the QCD medium created in heavy ion collisions at SPS, RHIC and LHC energies., Comment: proceeding for Quark Matter 2015, Kobe, Japan; 8 pages, 1 figures

We apply a Boltzmann approach to the kinetic regime of a relativistic Bose-Einstein condensate of scalar bosons by decomposing the one-particle distribution function in a condensate part and a non-zero momentum part of excited modes, leading to a coupled set of evolution equations which are then solved efficiently with an adaptive higher order Runge-Kutta scheme. We compare our results to the partonic cascade Monte-Carlo simulation BAMPS for a critical but far from equilibrium case of massless bosons. Motivated by the color glass condensate initial conditions in QCD with a strongly overpopulated initial glasma state, we also discuss the time evolution starting from an overpopulated initial distribution function of massive scalar bosons. In this system a self-similar evolution of the particle cascade with a non-relativistic turbulent scaling in the infrared sector is observed as well as a relativistic exponent for the direct energy cascade, confirming a weak wave turbulence in the ultraviolet region., Comment: 11 pages, 8 figures; v2: Ref. added, additional results, 12 figures

Both color screening and regeneration are hot medium effects on charmonium production in heavy ion collisions. While they affect in an opposite way the charmonium yield, their competition in transverse dynamics bring sensitivity to the ratio of averaged transverse momentum suqare for charmonium, which thus can reveal more nature of the QCD medium created from the collisions. We make an excitation analysis based on transport approach to illustrate such a picuture., Comment: 4 pages, 3 figures, SQM2015 proceedings

The early stage of high multiplicity pp, pA and AA collider is represented by a nearly quarkless, hot, deconfined pure gluon plasma. According to pure Yang-Mills Lattice Gauge Theory, this hot pure glue matter undergoes, at a high temperature, $T_c = 270$ MeV, a first order phase transition into a confined Hagedorn-GlueBall fluid. These new scenario should be characterized by a suppression of high $p_T$ photons and dileptons, baryon suppression and enhanced strange meson production. We propose to observe this newly predicted class of events at LHC and RHIC., Comment: 7 pages, 6 figures

We investigate charmonium production in Pb+Pb collisions at LHC beam energy $E_{\text {lab}}$=2.76 A TeV at fixed-target experiment ($\sqrt {s_{\text{NN}}}$=72 GeV). In the frame of a transport approach including cold and hot nuclear matter effects on charmonium evolution, we focus on the anti-shadowing effect on the nuclear modification factors $R_{AA}$ and $r_{AA}$ for the $J/\psi$ yield and transverse momentum. The yield is more suppressed at less forward rapidity ($y_\text{lab}\simeq$2) than that at very forward rapidity ($y_\text{lab}\simeq$4) due to the shadowing and anti-shadowing in different rapidity bins., Comment: 7 pages, 3 figures; submitted to Advances in High Energy Physics. arXiv admin note: text overlap with arXiv:1409.5559

We study the thermalization of gluons far from thermal equilibrium in relativistic kinetic theory. The initial distribution of gluons is assumed to resemble that in the early stage of ultrarelativistic heavy ion collisions. Only elastic scatterings in static, nonexpanding gluonic matter are considered. At first we show that the occurrence of condensation in the limit of vanishing particle mass requires a general constraint for the scattering matrix element. Then the thermalization of gluons with Bose-Einstein condensation is demonstrated in a transport calculation. We see a continuously increasing overpopulation of low energy gluons, followed by a decrease to the equilibrium distribution, when the condensation occurs. The times of the completion of the gluon condensation and of the entropy production are calculated. These times scale inversely with the energy density., Comment: 5 pages, 2 figures; references and clarifications added, version accepted by PRL

We investigate the {\Upsilon} production in heavy ion collisions at LHC energy in the frame of a dynamical transport approach. Both the initial production and in-medium regeneration and both the cold and hot nuclear matter effects are included in the calculations. In comparison with the ground state {\Upsilon}(1s), the excited state {\Upsilon}(2s) is much more sensitive to the heavy quark potential at finite temperature., Comment: proceeding for QM2014;4 pages, 5 figures

We investigate with a transport approach the cold and hot nuclear matter effects on the charmonium transverse momentum distributions in relativistic heavy ion collisions. The newly defined nuclear modification factor $r_{AA}=\langle p_{T}^2\rangle_{AA}/\langle p_{T}^2\rangle_{pp}$ and elliptic flow $v_2$ for $J/\psi$ are sensitive to the nature of the hot medium and the thermalization of heavy quarks. From SPS through RHIC to LHC colliding energies, we observe dramatic changes in the centrality dependence of $r_{AA}$. We find that at LHC energy, the finally observed charmonia are dominated by the regeneration from thermalized heavy quarks., Comment: 11 pages,10 figures

Color screening and regeneration are both hot medium effects on quarkonium production in high energy nuclear collisions. However, they affect in an opposite way the finally observed quarkonium spectra. Due to the competition of the two dynamical effects, the ratio of the integrated quarkonium yield between nuclear and elementary nucleon collisions loses its sensitivity. Once the information of quarkonium transverse motion is included, on the other hand, the ratio of averaged transverse momentum square reveals the nature of the QCD medium created in high energy nuclear collisions., Comment: 4 pages,4 figures

In comparison with J/\psi, the excited charmonium state \psi^\prime is loosely bounded and its yield is dominantly from the B-hadron decay. Based on the transport approach, we study the double ratio of N(\psi^\prime)/N(J/\psi) from A+A collisions to that from p+p collisions at LHC energy. We found that the primordial production in the initial stage and the regeneration in the hot medium are not significant for \psi^\prime production in heavy ion collisions at LHC and the double ratio in semi-central and central collisions is controlled by the B decay., Comment: 5 pages, 7 figures

The mean field effect in quark-gluon plasma on J/\psi production in heavy ion collisions is perturbatively calculated in a transport approach. While the global nuclear modification factor R_{AA} is not sensitive to the mean field, the reduced threshold for J/\psi regeneration leads to a significant enhancement of R_{AA} at low transverse momentum at RHIC and LHC energies., Comment: 6 pages, 4 figures

Heavy quark and charmonium production as well as their space-time evolution are studied in transport simulations of heavy-ion collisions at RHIC and LHC. In the partonic transport model Boltzmann Approach of MultiParton Scatterings (BAMPS) heavy quarks can be produced in initial hard parton scatterings or during the evolution of the quark-gluon plasma. Subsequently, they interact with the medium via binary scatterings with a running coupling and a more precise Debye screening which is derived from hard thermal loop calculations, participate in the flow and lose energy. We present results of the elliptic flow and nuclear modification factor of heavy quarks and compare them to available data. Furthermore, preliminary results on J/psi suppression at forward and mid-rapidity are reported for central and non-central collisions at RHIC. For this, we study cold nuclear matter effects and the dissociation as well as regeneration of J/psi in the quark-gluon plasma., Comment: 13 pages, 4 figures, proceedings of the XLIX International Winter Meeting on Nuclear Physics, 2011, Bormio, Italy; some typos corrected in v2