arXiv subjects

Gábor Papp

Publications and source records attributed to Gábor Papp.

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

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

Energy landscapes of small SK spin glasses

We study the $\pm J$ SK model for small $N$'s up to $N=9$. We sort the $2^{N(N-1)/2}$ possible realizations of the coupling matrix into equivalence classes according to the gauge symmetry and permutation symmetry and determine the energy spectra for each of these classes. We also study the energy landscape in these small systems and find that the elements of the hierarchic organization of ground states %and higher energy local minima start to appear in some samples already for $N$'s as small as 6.

cond-mat.dis-nn↗

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

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↗

arXiv2021

Optimizing Expected Shortfall under an $\ell_1$ constraint -- an analytic approach

Expected Shortfall (ES), the average loss above a high quantile, is the current financial regulatory market risk measure. Its estimation and optimization are highly unstable against sample fluctuations and become impossible above a critical ratio $r=N/T$, where $N$ is the number of different assets in the portfolio, and $T$ is the length of the available time series. The critical ratio depends on the confidence level $α$, which means we have a line of critical points on the $α-r$ plane. The large fluctuations in the estimation of ES can be attenuated by the application of regularizers. In this paper, we calculate ES analytically under an $\ell_1$ regularizer by the method of replicas borrowed from the statistical physics of random systems. The ban on short selling, i.e. a constraint rendering all the portfolio weights non-negative, is a special case of an asymmetric $\ell_1$ regularizer. Results are presented for the out-of-sample and the in-sample estimator of the regularized ES, the estimation error, the distribution of the optimal portfolio weights and the density of the assets eliminated from the portfolio by the regularizer. It is shown that the no-short constraint acts as a high volatility cutoff, in the sense that it sets the weights of the high volatility elements to zero with higher probability than those of the low volatility items. This cutoff renormalizes the aspect ratio $r=N/T$, thereby extending the range of the feasibility of optimization. We find that there is a nontrivial mapping between the regularized and unregularized problems, corresponding to a renormalization of the order parameters.

q-fin.PM↗

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↗

arXiv2018

Analytic approach to variance optimization under an $\ell_1$ constraint

The optimization of the variance supplemented by a budget constraint and an asymmetric $\ell_1$ regularizer is carried out analytically by the replica method borrowed from the theory of disordered systems. The asymmetric regularizer allows us to penalize short and long positions differently, so the present treatment includes the no-short-constrained portfolio optimization problem as a special case. Results are presented for the out-of-sample and the in-sample estimator of the regularized variance, the relative estimation error, the density of the assets eliminated from the portfolio by the regularizer, and the distribution of the optimal portfolio weights. We have studied the dependence of these quantities on the ratio $r$ of the portfolio's dimension $N$ to the sample size $T$, and on the strength of the regularizer. We have checked the analytic results by numerical simulations, and found general agreement. Regularization extends the interval where the optimization can be carried out, and suppresses the large sample fluctuations, but the performance of $\ell_1$ regularization is rather disappointing: if the sample size is large relative to the dimension, i.e. $r$ is small, the regularizer does not play any role, while for $r$'s where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless. We find that the $\ell_1$ regularization can eliminate at most half the assets from the portfolio, corresponding to this there is a critical ratio $r=2$ beyond which the $\ell_1$ regularized variance cannot be optimized: the regularized variance becomes constant over the simplex. These facts do not seem to have been noticed in the literature.

q-fin.PM↗

arXiv2018

Bias-variance trade-off in portfolio optimization under Expected Shortfall with $\ell_2$ regularization

The optimization of a large random portfolio under the Expected Shortfall risk measure with an $\ell_2$ regularizer is carried out by analytical calculation. The regularizer reins in the large sample fluctuations and the concomitant divergent estimation error, and eliminates the phase transition where this error would otherwise blow up. In the data-dominated region, where the number $N$ of different assets in the portfolio is much less than the length $T$ of the available time series, the regularizer plays a negligible role even if its strength $η$ is large, while in the opposite limit, where the size of samples is comparable to, or even smaller than the number of assets, the optimum is almost entirely determined by the regularizer. We construct the contour map of estimation error on the $N/T$ vs. $η$ plane and find that for a given value of the estimation error the gain in $N/T$ due to the regularizer can reach a factor of about 4 for a sufficiently strong regularizer.

q-fin.PM↗

arXiv2018

First Results with HIJING++ on High-energy Heavy Ion Collisions

We present preliminary results with HIJING++ (3.1.1) for identified hadron production in high-energy heavy ion collisions at LHC energies. The recently developed HIJING++ version is based on the latest version of PYTHIA8 and contains all the nuclear effects that have been included in the HIJING2.552, which will be improved by a new version of the shadowing parametrization and jet quenching module. Here, we summarize the structure and the speed gain due to parallelization of the new program code, also presenting some comparison between experimental data.

hep-ph↗

arXiv2017

First Results with HIJING++ in High-Energy Heavy-Ion Collisions

First calculated results with the new HIJING++ are presented for identified hadron production in high-energy heavy ion collisions. The recently developed HIJING++ version is based on the latest version of PYTHIA8 and contains all the nuclear effects has been included in the HIJING2.552, which will be improved by a new version of the shadowing parametrization and jet quenching module. Here, we summarize the major changes of the new program code beside the comparison between experimental data for some specific high-energy nucleus-nucleus collisions.

hep-ph↗

arXiv2017

Analytic solution to variance optimization with no short-selling

A large portfolio of independent returns is optimized under the variance risk measure with a ban on short positions. The no-short selling constraint acts as an asymmetric $\ell_1$ regularizer, setting some of the portfolio weights to zero and keeping the out of sample estimator for the variance bounded, avoiding the divergence present in the non-regularized case. However, the susceptibility, i.e. the sensitivity of the optimal portfolio weights to changes in the returns, diverges at a critical value $r=2$. This means that a ban on short positions does not prevent the phase transition in the optimization problem, it merely shifts the critical point from its non-regularized value of $r=1$ to $2$. At $r=2$ the out of sample estimator for the portfolio variance stays finite and the estimated in-sample variance vanishes. We have performed numerical simulations to support the analytic results and found perfect agreement for $N/T<2$. Numerical experiments on finite size samples of symmetrically distributed returns show that above this critical point the probability of finding solutions with zero in-sample variance increases rapidly with increasing $N$, becoming one in the large $N$ limit. However, these are not legitimate solutions of the optimization problem, as they are infinitely sensitive to any change in the input parameters, in particular they will wildly fluctuate from sample to sample. We also calculate the distribution of the optimal weights over the random samples and show that the regularizer preferentially removes the assets with large variances, in accord with one's natural expectation.

q-fin.PM↗

arXiv2015

Portfolio Optimization under Expected Shortfall: Contour Maps of Estimation Error

The contour maps of the error of historical resp. parametric estimates for large random portfolios optimized under the risk measure Expected Shortfall (ES) are constructed. Similar maps for the sensitivity of the portfolio weights to small changes in the returns as well as the VaR of the ES-optimized portfolio are also presented, along with results for the distribution of portfolio weights over the random samples and for the out-of-sample and in-the-sample estimates for ES. The contour maps allow one to quantitatively determine the sample size (the length of the time series) required by the optimization for a given number of different assets in the portfolio, at a given confidence level and a given level of relative estimation error. The necessary sample sizes invariably turn out to be unrealistically large for any reasonable choice of the number of assets and the confidence level. These results are obtained via analytical calculations based on methods borrowed from the statistical physics of random systems, supported by numerical simulations.

q-fin.RM↗

arXiv2015

Contour map of estimation error for Expected Shortfall

The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given confidence level and a given estimation error.

q-fin.RM↗

arXiv2014

Strong random correlations in networks of heterogeneous agents

Correlations and other collective phenomena in a schematic model of heterogeneous binary agents (individual spin-glass samples) are considered on the complete graph and also on 2d and 3d regular lattices. The system's stochastic dynamics is studied by numerical simulations. The dynamics is so slow that one can meaningfully speak of quasi-equilibrium states. Performing measurements of correlations in such a quasi-equilibrium state we find that they are random both as to their sign and absolute value, but on average they fall off very slowly with distance in all instances that we have studied. This means that the system is essentially non-local, small changes at one end may have a strong impact at the other. Correlations and other local quantities are extremely sensitive to the boundary conditions all across the system, although this sensitivity disappears upon averaging over the samples or partially averaging over the agents. The strong, random correlations tend to organize a large fraction of the agents into strongly correlated clusters that act together. If we think about this model as a distant metaphor of economic agents or bank networks, the systemic risk implications of this tendency are clear: any impact on even a single strongly correlated agent will spread, in an unforeseeable manner, to the whole system via the strong random correlations.

cond-mat.dis-nn↗