A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
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A deep neural network emulates lattice QCD equation of state within a quasi-particle model to compute QGP speed of sound, specific heat, viscosity, and conductivity at finite baryon chemical potential.
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Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC
A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
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Thermodynamic and Transport Properties of Quark-Gluon Plasma at Finite Chemical Potential with a DNN framework
A deep neural network emulates lattice QCD equation of state within a quasi-particle model to compute QGP speed of sound, specific heat, viscosity, and conductivity at finite baryon chemical potential.