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.
Heavy ion event generator HYDJET++ (HYDrodynamics plus JETs)
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
HYDJET++ is a Monte-Carlo event generator for simulation of relativistic heavy ion AA collisions considered as a superposition of the soft, hydro-type state and the hard state resulting from multi-parton fragmentation. This model is the development and continuation of HYDJET event generator (Lokhtin & Snigirev, 2006, EPJC, 45, 211). The main program is written in the object-oriented C++ language under the ROOT environment. The hard part of HYDJET++ is identical to the hard part of Fortran-written HYDJET and it is included in the generator structure as a separate directory. The soft part of HYDJET++ event is the "thermal" hadronic state generated on the chemical and thermal freeze-out hypersurfaces obtained from the parameterization of relativistic hydrodynamics with preset freeze-out conditions. It includes the longitudinal, radial and elliptic flow effects and the decays of hadronic resonances. The corresponding fast Monte-Carlo simulation procedure, C++ code FAST MC (Amelin et al., 2006, PRC, 74, 064901; 2008, PRC, 77, 014903) is adapted to HYDJET++. It is designed for studying the multi-particle production in a wide energy range of heavy ion experimental facilities: from FAIR and NICA to RHIC and LHC.
fields
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.