9 research outputs found
The QCD EoS of dense nuclear matter from Bayesian analysis of heavy ion collision data
Bayesian methods are used to constrain the density dependence of the QCD
Equation of State (EoS) for dense nuclear matter using the data of mean
transverse kinetic energy and elliptic flow of protons from heavy ion
collisions (HIC), in the beam energy range .
The analysis yields tight constraints on the density dependent EoS up to 4
times the nuclear saturation density. The extracted EoS yields good agreement
with other observables measured in HIC experiments and constraints from
astrophysical observations both of which were not used in the inference. The
sensitivity of inference to the choice of observables is also discussed.Comment: 10 pages, 10 figures, minor correction
Model dependence of the number of participant nucleons and observable consequences in heavy-ion collisions
The centrality determination and the estimated fluctuations of number of
participant nucleons in Au-Au collisions at 1.23 GeV beam kinetic
energy suffers from severe model dependencies. Comparing the Glauber Monte
Carlo (MC) and UrQMD transport models, it is shown that is a
strongly model dependant quantity. In addition, for any given centrality class,
Glauber MC and UrQMD predicts drastically different distributions.
The impact parameter and the number of charged particles on the
other hand are much more correlated and give an almost model independent
centrality estimator. It is suggested that the total baryon number balance,
from integrated rapidity distributions, can be used instead of in
experiments. Preliminary HADES data show significant differences to both, UrQMD
simulations and STAR data in this respect.Comment: 11 pages, 9 figure
NA61/SHINE online noise filtering using machine learning methods
The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings
Deep Learning Based Impact Parameter Determination for the CBM Experiment
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm
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
GeV 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 -0.3 to 0.2 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 -0.05 to 0.15 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.Comment: 9 pages, 8 figure
Deep learning based impact parameter determination for the CBM experiment
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm
An equation-of-state-meter for CBM using PointNet
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables
A chiral mean-field equation-of-state in UrQMD: effects on the heavy ion compression stage
It is shown that the initial compression in central heavy ion collisions at
beam energies of ~GeV depends dominantly on the
underlying equation of state and only marginally on the model used for the
dynamical description. To do so, a procedure to incorporate any equation of
state in the UrQMD transport model is introduced. In particular we compare the
baryon density, temperature and pressure evolution as well as produced entropy
in a relativistic ideal hydrodynamics approach and the UrQMD transport model,
where the same equation of state is used in both approaches. Not only is the
compression similar if the same equation of state is used in either dynamical
model, but it also strongly depends on the actual equation of state. These
results indicate that the equation of state can be studied with observables
which are sensitive to the initial compression phase and maximum compression
achieved in heavy ion collisions at these beam energies.Comment: 11 pages, 7 figures, version accepted by: The European Physical
Journal
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Proton and Λ flow and the equation of state at high density
Results on proton and Λ flow, calculated with the UrQMD model that incorporates different realistic density dependent equations of state, are presented. It is shown that the proton and hyperon flow shows sensitivity to the equation of state and especially to the appearance of a phase transition at densities below 4n0. Even though qualitatively hyperons and protons exhibit the same beam energy dependence of the flow, the quantitative results are different. In this context it is suggested that the hyperon measurements can be used to study the density dependence of the hyperon interaction in high density QCD matter