A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.
Fast hadron freeze-out generator, part II: noncentral collisions
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The fast Monte Carlo procedure of hadron generation developed in our previous work is extended to describe noncentral collisions of nuclei. We consider different possibilities to introduce appropriate asymmetry of the freeze-out hyper-surface and flow velocity profile. For comparison with other models and experimental data we demonstrate the results based on the standard parametrizations of the hadron freeze-out hyper-surface and flow velocity profile assuming either a common chemical and thermal freeze-out or the chemically frozen evolution from chemical to thermal freeze-out. The C++ generator code is written under the ROOT framework and is available for public use at http://uhkm.jinr.ru/
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hep-ph 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Simulations show non-flow two-particle cumulant distributions have high skewness and kurtosis while true elliptic flow distributions are closer to Gaussian with lower values.
citing papers explorer
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Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks
A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.
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Two-particle cumulant distribution: a simulation study of higher moments
Simulations show non-flow two-particle cumulant distributions have high skewness and kurtosis while true elliptic flow distributions are closer to Gaussian with lower values.