Simulations of OO and Ne-Ne collisions at 5.36 TeV show symmetry plane correlations that differ in a pattern consistent with tetrahedral 16O and deformed 20Ne nuclear shapes.
Acharyaet al.(ALICE Collaboration), Eur
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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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Nuclear geometry driven symmetry plane correlations in OO and Ne--Ne collisions at the Large Hadron Collider
Simulations of OO and Ne-Ne collisions at 5.36 TeV show symmetry plane correlations that differ in a pattern consistent with tetrahedral 16O and deformed 20Ne nuclear shapes.
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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.