arXiv subjects

Gábor Bíró

Publications and source records attributed to Gábor Bíró.

At least 19 recordsLinked to original sources

arXiv2026

Watts per event: evaluating Sustainability of HEP Event Generators beyond the LHC era

The development, tuning and operation of Monte Carlo event generators beyond the LHC era require vast amount of resources. In this study we investigate the sustainability of these software with a containerized set of tools (named 77rev/propripy), by benchmarking the HIJING++ heavy-ion Monte Carlo event generator. We analyze the performance of various CPU architectures and show that by choosing the level of multithreading properly, the cost of event generation can be optimized. The presented approach can reduce the energy footprint of high-energy physics event generators and therefore alleviate the ever-increasing, ubiquitous computational challenges.

physics.comp-ph↗

arXiv2026

Revisiting the soft-hard separation in the transverse momentum spectra of $pp$ collisions

We study the separation of soft and hard components in the transverse momentum spectra of charged particles as measured by ALICE in proton-proton collisions at $\sqrt{s}$ = 2.76, 5.02 and 13 TeV at the LHC. The soft component is described by a Boltzmann fit, while the residual spectra are identified as a hard QCD-like fragmentation contribution. After separation, the subtracted spectra show no significant evolution in shape or peak position with multiplicity, consistent with a two-component interpretation. Mean transverse momenta for both contributions remain nearly constant across multiplicity classes, while Pythia 8 Monte Carlo simulations confirm these trends. The robustness of the decomposition is demonstrated by a comparison between simulations with and without color reconnection, yielding consistent results. The terms `soft' and `hard' are used as operational labels within this framework and should not be interpreted as uniquely identified dynamical components. Our results are consistent with the two-component description as a viable and physically motivated alternative to hydrodynamical interpretations.

hep-ph↗

arXiv2026

CT Imaging with Helium and Carbon Ions for Hadron Therapy

Objective: To perform a comprehensive comparative analysis of proton, helium-ion, and carbon-ion computed tomography (CT) as direct imaging modalities for hadron therapy treatment planning, focusing on Relative Stopping Power (RSP) reconstruction accuracy and patient radiation dose. Approach: High-fidelity Monte Carlo simulations were conducted using the GATE/Geant4 platform to model a standard CTP404 phantom. RSP maps were reconstructed using an iterative Richardson-Lucy deconvolution algorithm. Imaging performance was evaluated by comparing reconstructed RSP values against ground truth data for various tissue-equivalent inserts, while integral doses were estimated for a human head geometry. Main results: All investigated particle modalities demonstrated a significant dose reduction compared to conventional X-ray CT protocols (which are approximately 40 mGy). The estimated imaging doses were 1.6 mGy for protons, 3.9 mGy for helium ions, and 22.7 mGy for carbon ions. In terms of accuracy, carbon-ion imaging achieved the highest fidelity for soft-tissue materials (mean absolute error <0.5\%). Helium ions offered a balanced performance with sub-1\% errors for most materials and a dose burden significantly lower than carbon ions. Protons exhibited the widest range of RSP deviations (-1.8\% to +3.1\%). Significance: Direct particle imaging eliminates the systematic uncertainties inherent in photon-to-hadron conversion. While carbon ions provide superior RSP reconstruction precision essential for complex treatment plans in heterogeneous anatomy, helium and proton imaging offer exceptional dose sparing, making them particularly advantageous for pediatric patients and frequent adaptive replanning scenarios.

physics.med-ph↗

arXiv2026

Proton Computed Tomography Image Reconstruction Based on the Richardson-Lucy Algorithm

Proton therapy is an emerging method in cancer therapy. One of the main developments is to increase the accuracy of the Bragg-peak position calculation, which requires more precise relative stopping power (RSP) measurements. A promising choice is the application of proton computed tomography (pCT) systems which takes the images under similar conditions, as they use the same irradiation device and hadron beam for imaging and treatment. A key aim is to develop a precise image reconstruction algorithm for pCT systems to reach their maximal performance. In this work, an iterative image reconstruction algorithm, based on the Richardson-Lucy iteration is proposed for the first time for proton CT image reconstruction. Monte Carlo (MC) simulations of CTP528 and CTP404 phantoms were used to benchmark the proposed method. In the case of an idealized detector setup, using a 1 mm pitch grid, 4.88 lp/cm spatial resolution and 0.66% average RSP uncertainty was achieved. The present method provides a promising proof-of-concept candidate for compromise between accuracy and speed with several further development directions.

physics.med-ph↗

arXiv2025

Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

astro-ph.IM↗

arXiv2024

Investigating the soft and hard limits in transverse momentum spectra in pp collisions

The transverse momentum spectra and their multiplicity dependence serve as key tools for extracting parameters to be compared with theoretical models. Over the past decade, the scientific community has extensively studied the possibility of a system analogous to quark-gluon plasma, predicted in heavy nuclei collisions, also existing in collisions involving light nuclei and protons. We have reanalysed the data published by the ALICE Collaboration at the LHC. We present the dependence of the mean transverse momenta obtained in the soft and soft+hard (mixed) parts. Finally, we also discuss possible refinements of the analyses concerning the use of statistical parameters of higher order, aimed at a more detailed way of comparing the models with data.

hep-ph↗

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↗

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

Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor

We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor and machine learning techniques are tested for extracting quantitative information from the images. By building a database of simulated signals with a range of plasma parameters for training Deep Neural Networks, we demonstrate that they can extract from the Schlieren images reliably and with high accuracy the location, the radius and the maximum ionization fraction of the plasma channel as well as the width of the transition region between the core of the plasma channel and the unionized vapor. We test several different neural network architectures with supervised learning and show that the parameter estimations supplied by the networks are resilient with respect to slight changes of the experimental parameters that may occur in the course of a measurement.

physics.plasm-ph↗

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↗

arXiv2020

Tsallis-thermometer: a QGP indicator for large and small collisional systems

The transverse momentum distribution of identified hadrons from recent years are analyzed within the thermodynamically consistent formulation of non-extensive statistics. A wide range of center-of-mass energies and average event multiplicities are studied for various hadron species. We demonstrate that the average event multiplicity is a key variable in the study of high-energy collisions and a smooth transition in the description from small to large systems is possible. For this purpose the non-extensive statistical approach is more than appropriate. The validity of the non-extensive description is explored in multiple ways: such as the calculation of integrated yields per unit rapidity, the analysis of radial flow and the consistency check of the thermodynamical variables. The 'Tsallis-thermometer' is introduced as an indicator of quark-gluon plasma in small collisional systems.

hep-ph↗

arXiv2019

Multiplicity Dependence in the Non-Extensive Hadronization Model Calculated by the HIJING++ Framework

The non-extensive statistical description of the identified final state particles measured in high energy collisions is well-known by its wide range of applicability. However, there are many open questions that need to be answered, including but not limited to, the question of the observed mass scaling of massive hadrons or the size and multiplicity dependence of the model parameters. This latter is especially relevant, since currently the amount of available experimental data with high multiplicity at small systems is very limited. This contribution has two main goals: On the one hand we provide a status report of the ongoing tuning of the soon-to-be-released HIJING++ Monte Carlo event generator. On the other hand, the role of multiplicity dependence of the parameters in the non-extensive hadronization model is investigated with HIJING++ calculations. We present cross-check comparisons of HIJING++ with existing experimental data to verify its validity in our range of interest as well as calculations at high-multiplicity regions where we have insufficient experimental data.

hep-ph↗

arXiv2019

Introducing HIJING++: the Heavy Ion Monte Carlo Generator for the High-Luminosity LHC Era

Beyond 2025 we will enter the High-Luminosity era of the LHC, right after the upgrades of the third Long Shutdown of the Large Hadron Collider (LHC). The ongoing state-of-the-art experimental instrument upgrades require high-performance simulation support in the background, that is modern, robust, and comes with long term support. The original FORTRAN based HIJING (Heavy Ion Jet INteraction Generator) has been used intensively since almost three decades by the high energy physics and heavy-ion community. However, it is getting overly challenging to conform to these new requirements. Our novel Monte Carlo event generator, the HIJING++ is the successor of the old FORTRAN version containing all the physics that its predecessor have. Moreover, among others a flexible module handling layer and an analysis interface is also introduced. This latter supports the most popular event container formats such as simple ascii, ROOT and the HepMC format, together with RIVET support. In this paper we compare the pre-release results of HIJING++ with proton-proton experimental data and Pythia8 calculations.

physics.comp-ph↗

arXiv2018

HIJING++, a Heavy Ion Jet INteraction Generator for the High-luminosity Era of the LHC and Beyond

HIJING++ (Heavy Ion Jet INteraction Generator) is the successor of the widely used original HIJING, developed almost three decades ago. While the old versions (1.x and 2.x) were written in FORTRAN, HIJING++ was completely rewritten in C++. During the development we keep in mind the requirements of the high-energy heavy-ion community: the new Monte Carlo software have a well designed modular framework, therefore any future modifications are much easier to implement. It contains all the physical models that were also present in it's predecessor, but utilizing modern C++ features it also includes native thread based parallelism, an easy-to-use analysis interface and a modular plugin system, which makes room for possible future improvements. In this paper we summarize the results of our performance tests measured on 2 widely used architectures.

physics.comp-ph↗

arXiv2017

Mass hierarchy and energy scaling of the Tsallis--Pareto parameters in hadron productions at RHIC and LHC energies

The latest, high-accuracy identified hadron spectra measurements in high-energy nuclear collisions led us to the investigation of the strongly interacting particles and collective effects in small systems. Since microscopical processes result in a statistical Tsallis-Pareto distribution, the fit parameters $q$ and $T$ are well suited for identifying system size scalings and initial conditions. Moreover, parameter values provide information on the deviation from the extensive, Boltzmann-Gibbs statistics in finite-volumes. We apply here the fit procedure developed in our earlier study for proton-proton collisions. The observed mass and $\sqrt{s}$ energy trends in the hadron production are compared to RHIC dAu and LHC pPb data in different centrality/multiplicity classes. Here we present new results on mass hierarchy in pp and pA from light to heavy hadrons.

hep-ph↗