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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Transverse momentum spectra in heavy-ion collisions exhibit universal scaling with multiplicity and mean pT, explained by Cooper-Frye hydrodynamics and equivalent to Hwa-Yang scaling.
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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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Deciphering the universal scaling of particle transverse momentum spectra in heavy-ion collisions
Transverse momentum spectra in heavy-ion collisions exhibit universal scaling with multiplicity and mean pT, explained by Cooper-Frye hydrodynamics and equivalent to Hwa-Yang scaling.