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Gabor Papp

Publications and source records attributed to Gabor Papp.

At least 19 recordsLinked to original sources
arXiv2023

Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model

Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional $q$-state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high-temperature (disordered) and low-temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high-dimensional systems.

physics.comp-ph↗
arXiv2022

Study of phase transition of Potts model with Domain Adversarial Neural Network

A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent $\nu$ by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost.

cond-mat.stat-mech↗
arXiv2021

Transfer learning of phase transitions in percolation and directed percolation

The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural network (DANN) based on transfer learning to studying non-equilibrium and equilibrium phase transition models, which are percolation model and directed percolation (DP) model, respectively. With the DANN, only a small fraction of input configurations (2d images) needs to be labeled, which is automatically chosen, in order to capture the critical point. To learn the DP model, the method is refined by an iterative procedure in determining the critical point, which is a prerequisite for the data collapse in calculating the critical exponent $\nu_{\perp}$. We then apply the DANN to a two-dimensional site percolation with configurations filtered to include only the largest cluster which may contain the information related to the order parameter. The DANN learning of both models yields reliable results which are comparable to the ones from Monte Carlo simulations. Our study also shows that the DANN can achieve quite high accuracy at much lower cost, compared to the supervised learning.

cond-mat.stat-mech↗
arXiv2021

Machine learning Hadron Spectral Functions in Lattice QCD

Hadron spectral functions carry all the information of hadrons and are encoded in the Euclidean two-point correlation functions. The extraction of hadron spectral functions from the correlator is a typical ill-posed inverse problem and infinite number of solutions to this problem exists. We propose a novel neural network (sVAE) based on the Variation Auto-Encoder (VAE) and Bayesian theorem. Inspired by the maximum entropy method (MEM) we construct the loss function of the neural work such that it includes a Shannon-Jaynes entropy term and a likelihood term. The sVAE is then trained to provide the most probable spectral functions. For the training samples of spectral function we used general spectral functions produced from the Gaussian Mixture Model. After the training is done we performed the mock data tests with input spectral functions consisting 1) only a free continuum, 2) only a resonance peak, 3) a resonance peak plus a free continuum and 4) a NRQCD motivated spectral function. From the mock data test we find that the sVAE in most cases is comparable to the maximum entropy method in the quality of reconstructing spectral functions and even outperforms the MEM in the case where the spectral function has sharp peaks with insufficient number of data points in the correlator. By applying to temporal correlation functions of charmonium in the pseudoscalar channel obtained in the quenched lattice QCD at 0.75 $T_c$ on $128^3\times96$ lattices and $1.5$ $T_c$ on $128^3\times48$ lattices, we find that the resonance peak of $\eta_c$ extracted from both the sVAE and MEM has a substantial dependence on the number of points in the temporal direction ($N_\tau$) adopted in the lattice simulation and $N_\tau$ larger than 48 is needed to resolve the fate of $\eta_c$ at 1.5 $T_c$.

hep-lat↗
arXiv2020

Scaling properties of jet-momentum profiles with multiplicity

We study the structure of jets in proton-proton collisions at LHC energies using \textsc{Pythia} 8 Monte Carlo simulations. We demonstrate that the radial jet profiles exhibit scaling properties with charged-hadron event multiplicity over a broad transverse-momentum range. We also provide parametrizations of the jet profiles based on different statistically-motivated analytical distributions. Based on this we propose that the scaling behavior stems from fundamental statistical properties of jet fragmentation.

hep-ph↗
arXiv2007

Intrinsic transverse momentum in di-hadron correlations

We study di-hadron correlations in proton-proton collisions at $\sqrt{s} = 200$ GeV and interpret experimental data in terms of a fragmentation width and a momentum imbalance. A fragmentation width of $580 \pm 50$ GeV/c is obtained, and the measured momentum imbalance gives an `intrinsic' transverse momentum width of partons in the proton of $2.6 \pm 0.2$ GeV/c. Consequences to heavy ion collisions are discussed.

hep-ph↗
arXiv2007

Random Levy Matrices Revisited

We compare eigenvalue densities of Wigner random matrices whose elements are independent identically distributed (iid) random numbers with a Levy distribution and maximally random matrices with a rotationally invariant measure exhibiting a power law spectrum given by stable laws of free random variables. We compute the eigenvalue density of Wigner-Levy (WL) matrices using (and correcting) the method by Bouchaud and Cizeau (BC), and of free random Levy (FRL) rotationally invariant matrices by adapting results of free probability calculus. We compare the two types of eigenvalue spectra. Both ensembles are spectrally stable with respect to the matrix addition. The discussed ensemble of FRL matrices is maximally random in the sense that it maximizes Shannon's entropy. We find a perfect agreement between the numerically sampled spectra and the analytical results already for matrices of dimension N=100. The numerical spectra show very weak dependence on the matrix size N as can be noticed by comparing spectra for N=400. After a pertinent rescaling spectra of Wigner-Levy matrices and of symmetric FRL matrices have the same tail behavior. As we discuss towards the end of the paper the correlations of large eigenvalues in the two ensembles are however different. We illustrate the relation between the two types of stability and show that the addition of many randomly rotated Wigner-Levy matrices leads by a matrix central limit theorem to FRL spectra, providing an explicit realization of the maximal randomness principle.

cond-mat.stat-mech↗
arXiv2005

Random Matrix Filtering in Portfolio Optimization

We study empirical covariance matrices in finance. Due to the limited amount of available input information, these objects incorporate a huge amount of noise, so their naive use in optimization procedures, such as portfolio selection, may be misleading. In this paper we investigate a recently introduced filtering procedure, and demonstrate the applicability of this method in a controlled, simulation environment.

physics.soc-ph↗
arXiv2003

Are Scale Free Networks Better?

We study the performance of Weibull and scale free Internet-like networks and compare them to a classical random graph based network. The scaling of the traffic load with the nodal degree is established, and confimed in a numerical simulation of the TCP traffic. The scaling allows us to estimate the link capacity upgrade required making and extra connection to an existing node.

cond-mat↗
arXiv2002

Differences in high p_t meson production between CERN SPS and RHIC heavy ion collisions

In this talk we present a perturbative QCD improved parton model calculation for light meson production in high energy heavy ion collisions. In order to describe the experimental data properly, one needs to augment the standard pQCD model by the transverse momentum distribution of partons ("intrinsic k_T"). Proton-nucleus data indicate the presence of nuclear shadowing and multiscattering effects. Further corrections are needed in nucleus-nucleus collisions to explain the observed reduction of the cross section. We introduce the idea of proton dissociation and compare our calculations with the SPS and RHIC experimental data.

nucl-th↗
arXiv2000

Effects of Dense Matter on Hadron Production in Heavy-Ion Collisions

The intrinsic transverse momentum distribution of partons in the nucleon can be used to explain a large amount of high-$p_T$ hadron and photon production data in high-energy nucleon-nucleon collisions at energies $\sqrt{s} \approx 20$ to 1800 GeV. However, proton-nucleus experiments at energies $\approx 30$ GeV show an extra enhancement (Cronin effect) in the yield of photons and mesons compared to a simple extrapolation of the proton-proton data. This enhancement is due to the effect of the dense hadronic matter encountered by the projectile proton in the nuclear environment. We discuss the origin and the properties of the nuclear enhancement.

nucl-th↗
arXiv1999

U(1) Problem at Finite Temperature

We model the effects of a large number of zero and near-zero modes in the QCD partition function by using sparse chiral matrix models with an emphasis on the quenched topological susceptibility in the choice of the measure. At finite temperature, the zero modes are not affected by temperature but are allowed to pair into topologically neutral near-zero modes which are gapped at high temperature. In equilibrium, chiral and U(1) symmetry are simultaneously restored for total pairing, evading mean-field arguments. We analyze a number of susceptibilities versus the light quark masses. At the transition point the topological susceptibility vanishes, and the dependence on the vacuum angle $θ$ drops out. Our results are briefly contrasted with recent lattice simulations.

hep-lat↗
arXiv1999

Chiral Disorder in Two-Color QCD with Abelian External Fluxes

We investigate the effects of several Abelian external fluxes $\phi$, on the Euclidean Dirac spectrum of light quarks in QCD with two colors. Our results provide for a novel way of assessing the pion weak decay constant directly from spectral fluctuations, and discriminating a flux-rich from a flux-poor vacuum, using QCD lattice simulations.

hep-lat↗
arXiv1999

The phase diagram and bulk thermodynamical quantities in the NJL model at finite temperature and density

We reexamine the recent instanton motivated studies of Alford, Rajagopal and Wilczek, and Berges and Rajagopal in the framework of the standard SU(2) Nambu-Jona-Lasinio model. The chiral phase diagram is calculated in the temperature--density plane, and the pressure is evaluated as the function of the density. Obtaining simple approximate relations describing the $T$-$μ$ and $T$-$p_F$ phase transition lines we find that the results of the instanton based model and that of the NJL model are identical. The diquark transition line is also given.

nucl-th↗
arXiv1999

Green's Functions in Non-hermitian Random Matrix Models

We review some recent techniques for dealing with non-hermitian random matrix models based on generalized Green's functions. We introduce the diagrammatic methods in the hermitian case and generalize them to the non-hermitian case. The results are illustrated in terms of the eigenvalue distribution, eigenvector statistics and addition laws.

cond-mat.dis-nn↗
arXiv1999

$θ$ Vacuum in a Random Matrix Model

Inspired by recent lattice calculations, we model certain aspects of the $θ$-vacuum using a matrix model with gaussian weights. The vacuum energy exhibits a cusp at $θ<π$ that is sensitive to both the accuracy of the numerical analysis and the maximum density of winding modes present in a finite volume.

hep-ph↗
arXiv1999

Chiral Disorder and Diffusion in The QCD Vacuum

In this talk, which popularizes some of our recent work, we provide novel insights into the bulk properties of light chiral quarks in a fixed Euclidean volume (e.g. lattice QCD). We show that the spontaneous breakdown of chiral symmetry results into diffusing quarks with a vacuum diffusion constant $D=2F_π^2/|<\bar{q}q>|$ $\approx 0.22$ fm, in striking analogy to diffusing electrons in disordered metals in one-, two- and three-dimensions.

hep-ph↗