arXiv subjects

Jan Steinheimer

Publications and source records attributed to Jan Steinheimer.

At least 73 records · Page 4Linked to original sources

arXiv2021

Machine learning based approach to fluid dynamics

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.

physics.comp-ph↗

arXiv2021

Repulsive properties of hadrons in lattice QCD data and neutron stars

Second-order susceptibilities $χ^{11}_{ij}$ of baryon, electric, and strangeness, $B$, $Q$, and $S$, charges, are calculated in the Chiral Mean Field (CMF) model and compared to available lattice QCD data. The susceptibilities are sensitive to the short range repulsive interactions between different hadron species, especially to the hardcore repulsion of hyperons. Decreasing the hyperons size, as compared to the size of the non-strange baryons, does improve significantly the agreement of the CMF model results with the Lattice QCD data. The electric charge-dependent susceptibilities are sensitive to the short range repulsive volume of mesons. The comparison with lattice QCD data suggests that strange baryons, non-strange mesons and strange mesons have significantly smaller excluded volumes than non-strange baryons. The CMF model with these modified hadron volumes allows for a mainly hadronic description of the QCD susceptibilities significantly above the chiral pseudo-critical temperature. This improved CMF model which is based on the lattice QCD data, has been used to study the properties of both cold QCD matter and neutron star matter. The phase structure in both cases is essentially unchanged, i.e. a chiral first-order phase transition occurs at low temperatures ($T_{\rm CP}\approx 17$ MeV), and hyperons survive deconfinement to higher densities than non-strange hadrons. The neutron star maximal mass remains close to 2.1$M_\odot$ and the mass-radius diagram is only modified slightly due to the appearance of hyperons and is in agreement with astrophysical observations.

hep-ph↗

arXiv2021

Proton correlations and apparent intermittency in the UrQMD model with hadronic potentials

It is shown that the inclusion of hadronic interactions, and in particular nuclear potentials, in simulations of heavy ion collisions at the SPS energy range can lead to obvious correlations of protons. These correlations contribute significantly to an intermittency analysis as performed at the NA61 experiment. The beam energy and system size dependence is studied by comparing the resulting intermittency index for heavy ion collisions of different nuclei at beam energies of $40A$, $80A$ and $150A$ GeV. The resulting intermittency index from our simulations is similar to the reported values of the NA61 collaboration, if nuclear interactions are included. The observed apparent intermittency signal is the result of the correlated proton pairs with small relative transverse momentum $Δp_{t}$, which would be enhanced by hadronic potentials, and this correlation between the protons is slightly influenced by the coalescence parameters and the relative invariant four-momentum $q_{inv}$ cut.

nucl-th↗

arXiv2021

Ambiguities in the hadro-chemical freeze-out of Au+Au collisions at SIS18 energies and how to resolve them

The thermal fit to preliminary HADES data of Au+Au collisions at $\sqrt{s_{_{NN}}}=2.4$ GeV shows two degenerate solutions at $T\approx50$ MeV and $T\approx70$ MeV. The analysis of the same particle yields in a transport simulation of the UrQMD model yields the same features, i.e. two distinct temperatures for the chemical freeze-out. While both solutions yield the same number of hadrons after resonance decays, the feeddown contribution is very different for both cases. This highlights that two systems with different chemical composition can yield the same multiplicities after resonance decays. The nature of these two minima is further investigated by studying the time-dependent particle yields and extracted thermodynamic properties of the UrQMD model. It is confirmed, that the evolution of the high temperature solution resembles cooling and expansion of a hot and dense fireball. The low temperature solution displays an unphysical evolution: heating and compression of matter with a decrease of entropy. These results imply that the thermal model analysis of systems produced in low energy nuclear collisions is ambiguous but can be interpreted by taking also the time evolution and resonance contributions into account.

hep-ph↗

arXiv2020

A fast centrality-meter for heavy-ion collisions at the CBM experiment

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.

hep-ph↗

arXiv2020

Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

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.

nucl-th↗

arXiv2020

Critical point fluctuations: Finite size and global charge conservation effects

We investigate simultaneous effects of finite system size and global charge conservation on thermal fluctuations in the vicinity of a critical point. For that we consider a finite interacting system which exchanges particles with a finite reservoir (thermostat), comprising a statistical ensemble that is distinct from the common canonical and grand canonical ensembles. As a particular example the van der Waals model is used. The global charge conservation effects strongly influence the cumulants of particle number distribution when the system size is comparable to that of the reservoir. If the system size is large enough to capture all the physics associated with the interactions, the global charge conservation effects can be accurately described and corrected for analytically, within a recently developed subensemble acceptance method. The finite size effects start to play a significant role when the correlation length grows large due to proximity of the critical point or when the system is small enough to be comparable to an eigenvolume of an individual particle. We discuss our results in the context of fluctuation measurements in heavy-ion collisions.

hep-ph↗

arXiv2020

Unsupervised Outlier Detection in Heavy-Ion Collisions

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.

hep-ex↗

arXiv2020

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

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.

hep-ph↗

arXiv2020

Influence of centrality definition and detector efficiency on the net-proton kurtosis

We study the influence of the centrality definition and detector efficiency on the net-proton kurtosis for minimum bias Au+Au collisions at a beam energy of $\sqrt{s_{\mathrm{NN}}}= 7.7$ GeV by using the UrQMD model. We find that different ways of defining the centrality lead to different cumulant ratios. Moreover, we demonstrate that the kurtosis is suppressed for central collisions when a wider transverse momentum acceptance is used. Finally, the influence of a detector efficiency on the measured cumulant ratios is estimated.

nucl-th↗

arXiv2020

Elliptic flow splitting between protons and antiprotons from hadronic potentials

The difference in elliptic flow $v_{2}$ between protons and antiprotons, produced in $^{197}\text{Au}+^{197}\text{Au}$ collisions at center-of-mass energies $\sqrt{s_{NN}}=5-12~\text{GeV}$, is studied within a modified version of the ultrarelativistic quantum molecular dynamics (UrQMD) model. Two different model scenarios are compared: the cascade mode and the mean field mode which includes potential interactions for both formed and pre-formed hadrons. The model results for the elliptic flow of protons and the relative $v_{2}$ difference between protons and antiprotons obtained from the mean field mode agree with the available experimental data, while the $v_{2}$ difference is near zero for the cascade mode. Our results show that the elliptic flow splitting, observed for particles and antiparticles, can be explained by the inclusion of proper hadronic interactions. In addition, the difference in $v_{2}$ between protons and antiprotons depends on the centrality and the rapidity window. With smaller centrality and/or rapidity acceptance, the observed elliptic flow splitting is more sensitive to the beam energy, indicating a strong net baryon density dependence of the effect. We propose to confirm this splitting at the upcoming experiments from Beam Energy Scan (BES) Phase-\Rmnum{2} at Relativistic Heavy Ion Collider (RHIC), the Compressed Baryonic Matter (CBM) at Facility for Antiproton and Ion Research (FAIR), High Intensity heavy ion Accelerator Facility (HIAF) and Nuclotron-based Ion Collider fAcility (NICA).

nucl-th↗

arXiv2020

QCD equation of state at vanishing and high baryon density: Chiral Mean Field model

The thermodynamic properties of high temperature and high density QCD-matter are studied using the Chiral SU(3)-flavor parity-doublet Polyakov-loop quark-hadron mean-field model, CMF. The CMF model provides a proper description of lattice QCD data, heavy-ions physics, and static neutron stars. The behavior of lines of constant pressure with increase of baryon density is discussed. The rapid change of pressure behavior at $μ_B/T\approx3$ suggests a strong contribution of baryons to thermodynamic properties at this region. The position of this region is very close to the radius of convergence for a Taylor expansion of the QCD pressure. The role of mesons and unstable hadrons in the hydrodynamic expansion of strongly interacting matter is also discussed.

hep-ph↗

arXiv2020

The QCD phase diagram and statistics friendly distributions

The preliminary STAR data for proton cumulants for central collisions at \sqrt{s}=7.7 GeV are consistent with a two-component proton multiplicity distribution. We show that this two-component distribution is statistics friendly in that factorial cumulants of surprisingly high orders may be extracted with a relatively small number of events. As a consequence the two-component model can be tested and verified right now with the presently available STAR data from the first phase of the RHIC beam energy scan.

nucl-th↗

arXiv2019

Constraining resonance properties through kaon production in pion-nucleus collisions at low energies

Hadronic interactions are crucial for the dynamical description of heavy-ion reactions at low collision energies and in the late dilute stages at high collision energies. In particular, the properties and decay channels of resonances are an essential ingredient of hadronic transport approaches. The HADES collaboration measured particle production in collisions of pions with carbon and tungsten nuclei at $E_\text{kin} = 1.7\,\text{GeV}$. Such reactions are of high interest, because they allow to probe the properties of baryonic resonances produced in a much cleaner environment than the usual nucleus-nucleus collisions. We study these reactions with two transport approaches: SMASH (Simulating Many Accelerated Strongly-interacting Hadrons) and UrQMD (Ultra relativistic Quantum Molecular Dynamics) which follow the same underlying concept but with different implementations. The differential spectra in rapidity and transverse momentum are used to show how model parameters, as the decay channels of high mass resonances and angular distributions of kaon elastic scattering, can be constrained. It is found that the data favor the production of more particles with lower momentum over the production of few particles with higher momentum in these decays. In addition, the shape of the rapidity distribution of the kaons strongly depends on the angular distribution of the elastic kaon-nucleon cross section.

nucl-th↗

arXiv2019

A machine learning study to identify spinodal clumping in high energy nuclear collisions

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.

nucl-th↗

arXiv2019

Backward nucleon production by heavy baryonic resonances in proton-nucleus collisions

The production of backward nucleons, $N(180^\circ)$, at $180^\circ$ in the nuclear target rest frame in proton-nucleus ($\mathrm{p}+A$) collisions is studied. The backward nucleons appearing outside of the kinematically allowed range of proton-nucleon ($\mathrm{p}+N$) reactions are shown to be due to secondary reactions of heavy baryonic resonances produced inside the nucleus. Baryonic resonances $R$ created in primary $\mathrm{p}+N$ reactions can change their masses and momenta due to successive collisions $R+N\rightarrow R +N $ with other nuclear nucleons. Two distinct mechanisms and kinematic restrictions are studied: the reaction $R+N\rightarrow N(180^\circ)+N$ and the resonance decay $R\rightarrow N(180^\circ)+π$. Simulations of $\mathrm{p}+A$ collisions using the Ultra-relativistic Quantum Molecular Dynamics model support these mechanisms and are consistent with available data on proton backward production.

nucl-th↗

arXiv2019

Matter And Gravitation In Collisions of heavy ions and neutron stars: equation of state

The gravitational waves emitted from a binary neutron star merger, as predicted from general relativistic magneto-hydrodynamics calculations, are sensitive to the appearance of quark matter and the stiffness of the equation of state of QCD matter present in the inner cores of the stars. This is a new messenger observable from outer space, which does provide direct signals for the phase structure of strongly interacting QCD matter at high baryon density and high temperature. These astrophysically created extremes of thermodynamics do match, to within 20\%, the values of densities and temperatures which we find in relativistic hydrodynamics and transport theory of heavy ion collisions at the existing laboratories, if though at quite different rapidity windows, impact parameters and bombarding energies of the heavy nuclear systems. We demonstrate how one unified equation of state can be constructed and used for both neutron star physics and hot QCD matter excited at laboratory facilities. The similarity in underlying QCD physics allows the gravitational wave signals from future advanced LIGO and Virgo events to be combined with the analysis of high multiplicity fluctuations and flow measurements in heavy ion detectors in the lab to pin down the EoS and the phase structure of dense matter.

hep-ph↗

arXiv2019

First, second, third and fourth flow harmonics of deuterons and protons in Au+Au reactions at 1.23 A GeV

We explore the directed, elliptic, triangular and quadrangular flow of deuterons in Au+Au reactions at a beam energy of 1.23 AGeV within the UrQMD approach. These investigations are of direct relevance for the HADES experiment at GSI that has recently presented first data on the flow of light clusters in Au+Au collisions at 1.23 AGeV. To address the deuteron flow, UrQMD has been extended to include deuteron formation by coalescence. We find that this ansatz provides a very good description of the measured deuteron flow data, if a hard equation of state is used for the simulation. In addition we show that light cluster formation has a sizable impact on the proton flow and has to be taken into account to obtain reliable results in the forward/backward region. Based on the observed scaling of the flow, which is a natural result of coalescence, we conclude that deuteron production at GSI energies is a final state recombination effect. Finally, we also discuss the scaling relations of the higher order flow components up to $v_4$. We show that $v_3 \sim v_1v_2$ and $v_4 \sim v_2^2$ as function of transverse momentum and that the integrated $v_2^2 \sim v_4$ over the investigated energy range from $E_{lab}$=0.1 AGeV to 40 AGeV.

nucl-th↗