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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
Akash Das, Satya Ranjan Nayak, B. K. Singh
In this study, we employ a conventional deep neural network (NN) framework integrated with physics-based constraints to predict charged hadron multiplicity () in hea…
Study of Particle Production in Au+Au Collisions at 11.5-200 GeV Using HYDJET++ Framework
Gauri Devi, Satya Ranjan Nayak, B. K. Singh
Using the HYDJET++ model, we study the production of multi-strange particles( and ) in Au+Au collisions at 11.5, 19.6, 27, 39, and 200 GeV as functions of t…
Two-particle cumulant distribution: a simulation study of higher moments
Satya Ranjan Nayak, Akash Das, B. K. Singh
In this work, we have shown the two-particle correlations of charged hadrons in d-Au collisions at 200 GeV in PYTHIA8/Angantyr simulations. These correlations were studied at diffe…
Beam energy dependence of transverse momentum distribution and elliptic flow in Au-Au collisions using HYDJET++ model
Satya Ranjan Nayak, Saraswati Pandey, B. K. Singh
In this work, we present the particle ratios, transverse momentum spectra, and elliptic flow () of , and in Au-Au collisions at 62…