arXiv subjects

Gergely Gábor Barnaföldi

Publications and source records attributed to Gergely Gábor Barnaföldi.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies

We developed a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions from RHIC to LHC energies. The success of our model is mainly the estimation of $v_2$ from final state particle kinematic information and learning the centrality and the transverse momentum ($p_{\rm T}$) dependence of $v_2$ in wide $p_{\rm T}$ regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events. We present $v_2$ estimates for $π^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at various LHC energies. These results are compared with the available experimental data wherever possible.

hep-ph↗

arXiv2024

Application of Kaluza-Klein Theory in Modeling Compact Stars: Exploring Extra Dimensions

A theoretical framework for calculating the mass-radius curve of compact stars in the Kaluza-Klein space-time is introduced, with one additional compact spatial dimension. Static, spherically symmetric solutions are considered, with the equation of state provided by a zero temperature, interacting multidimensional Fermi gas. To model the strong force between baryons, a repulsive potential is introduced, which is linear in the particle number density. The maximal mass of compact stars is calculated for different model parameters, and with a physical parameter choice, it satisfies observational data, meaning that it is possible to model simple, realistic objects within this framework. Based on this comparison, a limiting size for the observational regime of extra dimensions in compact stars is provided, with $r_c \gtrsim 0.2$~fm.

gr-qc↗

arXiv2024

Evolution of the hot dense matter at LHC energies through light and heavy-flavor hadrons using non-extensive thermodynamics

We investigate the formation and evolution of hot systems comprising charmed and light hadrons using non-extensive thermodynamics. We analyze data from pp, p--Pb, and Pb--Pb collisions at center-of-mass energies ranging from $\sqrt{s_{\rm NN}} = 2.76$~TeV to 13~TeV, measured by the CERN LHC ALICE experiment. The hadron species examined include charged pions and kaons, ${\rm K}^0_{\rm s}$, (anti)protons, $ϕ$ mesons, $Λ^0$ hyperons, and D mesons. Employing our previously established methods, we determine the common Tsallis parameters $T_{\rm eq}$ and $q_{\rm eq}$ for each hadron type. While charm comes from earlier than light hadrons, we see that $T_{\rm eq}$ is ordered by mass, reflecting a similar ordering in the time-scale relevant for the spectrum. Our results also allow for constraining the heat capacity of the system. The current analysis thus enhances our understanding of hadron production dynamics and thermal properties in high-energy collisions.

hep-ph↗

arXiv2024

Estimating event-by-event multiplicity by a Machine Learning Method for Hadronization Studies

Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, the prediction results of three trained ResNet networks are presented, by investigating charged particle multiplicities at event-by-event level. The widely used Lund string fragmentation model is applied as a training-baseline at $\sqrt{s}= 7$ TeV proton-proton collisions. We found that neural-networks with $ \gtrsim\mathcal{O}(10^3)$ parameters can predict the event-by-event charged hadron multiplicity values up to $ N_\mathrm{ch}\lesssim 90 $.

hep-ph↗

arXiv2024

Characterisation of plastic scintillator paddles and lightweight MWPCs for the MID subsystem of ALICE 3

The ALICE collaboration is proposing a completely new detector, ALICE 3, for operation during the LHC Runs 5 and 6. One of the ALICE~3 subsystems is the Muon IDentifier detector (MID), which has to be optimised to be efficient for the reconstruction of $J/ψ$ at rest (muons down to $p_{\rm T}\approx1.5$ GeV/$c$) for $|η|<1.3$. Given the modest particle flux expected in the MID of a few Hz/cm$^2$, technologies like plastic scintillator bars ($\approx1$ m length) equipped with wavelength-shifting fibers and silicon photomultiplier readout, and lightweight Multi-Wire Proportional Chambers (MWPCs) are under investigation. To this end, different plastic scintillator paddles and MWPCs were studied at the CERN T10 test beam facility. This paper reports on the performance of the scintillator prototypes tested at different beam momenta (from 0.5 GeV/$c$ up to 6 GeV/$c$) and positions (horizontal, vertical, and angular scans). The MWPCs were tested at different momenta (from 0.5 GeV/$c$ to 10 GeV/$c$) and beam intensities, their efficiency and position resolutions were verified beyond the particle rates expected with the MID in ALICE 3.

physics.ins-det↗

arXiv2024

Decay rate measurements $^{137}$Cs at Jánossy Underground Research Laboratory

The question whether an annual modulation is observable during nuclear decay rate measurements has long been the subject of research. One of the possible explanations for the annual variations would be the effect of solar neutrinos, the flux of which changes in correlation with the Earth-Sun distance. A decay rate measurement with a $^{137}$Cs source and a HPGe detector is currently being conducted 30 meters below the ground at Jánossy Underground Research Laboratory (Csillebérc, Hungary). The laboratory is part of the Vesztergombi High Energy Laboratory (VLAB), one of the TOP 50 research infrastructures in Hungary. From October 2022 to March 2023, data of six months' worth has been collected, and hence this is a new opportunity to check whether the annual variation in decay rate can be observed. The laboratory, the experiment, the data processing method, and the first results are presented in this study.

nucl-ex↗

arXiv2023

3D Muographic Inversion in the Exploration of Cavities and Low-density Fractured Zones

Muography is an imaging tool based on the attenuation of cosmic muons to observe the density distribution of large objects, such as underground caves or fractured zones. Tomography based on muography measurements -- that is, three dimensional reconstruction of density distribution from two dimensional muon flux maps -- brings up special challenges. The detector field of view covering must be as balanced as possible, considering the muon flux drop at higher zenith angles and the detector placement possibilities. The inversion from directional muon fluxes to 3D density map is usually underdetermined (more voxels than measurements) which can be unstable due to partial coverage. This can be solved by geologically relevant Bayesian constraints. The Bayesian principle results in parameter bias and artifacts. In this work, the linearized (density-length based) inversion is applied, the methodology is explained, formulating the constraints associated with inversion to ensure the stability of parameter fitting. After testing the procedure on synthetic examples, an actual high quality muography measurement data set from 7 positions is used as input for the inversion. The result demonstrates the tomographic imaging of a complex karstic crack zone and provides details on the complicated internal structures. The existence of low density zones in the imaged space was verified by samples from core drills, which consist altered dolomite powder within the intact high density dolomite.

physics.geo-ph↗

arXiv2023

Challenges in the thermal modeling of highly porous carbon foams

The heat pulse experiment is a well-known method for determining thermal diffusivity. However, neither the measurement nor the evaluation methodologies are straightforward for heterogeneous, highly porous materials. In the present paper, we focus on two open-cell carbon foam types, differing in their porosity but having the same size. Recent experiments showed that a non-Fourier behaviour, called 'over-diffusive' propagation, is probably present for such a complex structure. The (continuum) Guyer-Krumhansl equation stands as a promising candidate to model such transient thermal behaviour. In order to obtain a reliable evaluation and thus reliable thermal parameters, we utilize a novel, state-of-the-art evaluation procedure developed recently using an analytical solution of the Guyer-Krumhansl equation. Based on our observations, it turned out that the presence of high porosity alone is necessary but not satisfactory for non-Fourier behaviour. Additionally, the mentioned non-Fourier effects are porosity-dependent. However, porous samples can also follow the Fourier law. These data serve as a basis to correctly identify the characteristic heat transfer mechanisms and their corresponding time scales, which altogether result in the present non-Fourier behaviour. Keywords: flash experiments, non-Fourier heat conduction, highly porous carbon foams.

physics.class-ph↗

arXiv2023

Exploration of Differentiability in a Proton Computed Tomography Simulation Framework

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary.

physics.med-ph↗

arXiv2023

Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies

Recent developments on a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions have shown us the prediction power of this technique. The success of the model is mainly the estimation of $v_2$ from final state particle kinematic information and learning the centrality and the transverse momentum ($p_{\rm T}$) dependence of $v_2$. The deep learning model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with a multiphase transport model (AMPT). We extend this work to estimate $v_2$ for light-flavor identified particles such as $π^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at RHIC and LHC energies. The number of constituent quark (NCQ) scaling is also shown. The evolution of $p_{\rm T}$-crossing point of $v_2(p_{\rm T})$, depicting a change in meson-baryon elliptic flow at intermediate-$p_{\rm T}$, is studied for various collision systems and energies. The model is further evaluated by training it for different $p_{\rm T}$ regions. These results are compared with the available experimental data wherever possible.

hep-ph↗

arXiv2023

Dedicated Analysis Facility for HEP Experiments

High-energy physics (HEP) provides ever-growing amount of data. To analyse these, continuously-evolving computational power is required in parallel by extending the storage capacity. Such developments play key roles in the future of this field however, these can be achieved also by optimization of existing IT resources. One of the main computing capacity consumers in the HEP software workflow are detector simulation and data analysis. To optimize the resource requirements for these aims, the concept of a dedicated Analysis Facility (AF) for Run 3 has been suggested by the ALICE experiment at CERN. These AFs are special computing centres with a combination of CPU and fast interconnected disk storage modules, allowing for rapid turnaround of analysis tasks on a dedicated subset of data. This in turn allows for optimization of the analysis process and the codes before the analysis is performed on the large data samples on the Worldwide LHC Computing Grid. In this paper, the structure and the progress summary of the Wigner Analysis Facility (Wigner AF) is presented for the period 2020-2022.

cs.DC↗

arXiv2023

Machine Learning based KNO-scaling of charged hadron multiplicities with Hijing++

The scaling properties of the final state charged hadron and mean jet multiplicity distributions, calculated by deep residual neural network architectures with different complexities are presented. The parton-level input of the neural networks are generated by the Hijing++ Monte Carlo event generator. Hadronization neural networks, trained with $\sqrt{s}=7$ TeV events are utilized to perform predictions for various LHC energies from $\sqrt{s}=0.9$ TeV to 13 TeV. KNO-scaling properties were adopted by the networks at hadronic level.

hep-ph↗

arXiv2022

Testing of KNO-scaling of charged hadron multiplicities within a Machine Learning based approach

The results of a Machine Learning-based method is presented here to investigate the scaling properties of the final state charged hadron and mean jet multiplicity distributions. Deep residual neural network architectures with different complexities are utilized to predict the final state multiplicity distribution from the parton-level final state, generated by the \textsc{Pythia} Monte Carlo event generator. Hadronization networks were trained by $\sqrt{s}=7$ TeV events, while predictions have been made for various LHC energies from $\sqrt{s}=0.9$ TeV to 13 TeV. Scaling properties were adopted by the networks at hadronic level, indeed KNO-scaling is preserved -- although, the scaling of the mean jet multiplicity distributions varies for the applied models.

hep-ph↗

arXiv2022

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.

nucl-th↗

arXiv2022

Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.

hep-ph↗

arXiv2022

Estimating the values and variations of neutron star observables by dense nuclear matter properties

Recent NICER observation data on PSR J0030+0451 has recently added a unique mass-radius constraint on the properties of the superdense nuclear matter existing in the interior of compact stars. Such a macroscopic data restrict further the microscopical models, elementary interactions, and their parameters, however, with reasonable margin because of the masquarade problem. Here our goal is to identify the origin and quantify the magnitude of the theoretical uncertainties of the nuclear matter models. A detailed study on the effect of different interaction terms and the nuclear parameter values in the Lagrangian of the extended $σ$-$ω$ model is presented here. The equation of state was inserted to the Tolman--Oppenheimer--Volkoff equation and observable parameters of the neutron star were calculated. We identified, that the optimal Landau effective mass is the most relevant physical parameter modifying the macroscopic observable values. Moreover, the compressibility and symmetry energy terms just generate one-one order of magnitude smaller effect to this, respectively. We calculated the linear relations between the maximal mass of a compact star and these microscopic nuclear parameter values within the physical relevant parameters range. Based on the mass observational data we estimated the magnitude of the radii of PSR J1614-2230, PSR J0348+0432, and PSR J0740+6620 including theoretical uncertainties arising from the models' interaction terms and their parameter values choices.

astro-ph.HE↗

arXiv2022

Studying Hadronization by Machine Learning Techniques

Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, results of two ResNet networks are presented by investigating global and kinematical quantities, indeed jet- and event-shape variables. The widely used Lund string fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton collisions to predict the most relevant observables at further LHC energies.

hep-ph↗

arXiv2021

Scaling Hydrodynamical Evolution of a Gravitating Dark-fluid Universe

We present a dark fluid model which contains the general linear equation of state including the gravitation term. The obtained spherical symmetric Euler equation and the continuity equation was investigated with the Sedov-type time-dependent self-similar {\it ansatz} which is capable to describe physically relevant diffusive and dispersive solutions. %The role of the parameter in the equation of state is investigated. As results the space and time dependent fluid density and radial velocity fields are presented and analyzed. Additionally, the role of the initial velocity on the kinetic and total energy densities of the fluid is discussed.

gr-qc↗