Bayesian constraints on early-time jet quenching from large collision systems yield predictions of measurable energy loss in oxygen-oxygen collisions.
Acharyaet al.(ALICE), Phys
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A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
The first measurement of charged-particle dijet invariant mass spectra shows no significant nuclear modification in p-Pb collisions at low masses.
Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.
Jet nuclear modification factor R_AA increases with cone radius R as in-cone energy loss from elastic recoils and radiated gluons decreases at larger radii.
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A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions
A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.