Bayesian constraints on early-time jet quenching from large collision systems yield predictions of measurable energy loss in oxygen-oxygen collisions.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A deep neural network interpolates and extrapolates proton-proton reference transverse-momentum spectra to unmeasured center-of-mass energies using ALICE LHC data.
citing papers explorer
-
Bayesian Constraints on Pre-Equilibrium Jet Quenching and Predictions for Oxygen Collisions
Bayesian constraints on early-time jet quenching from large collision systems yield predictions of measurable energy loss in oxygen-oxygen collisions.
-
DNN predictions for pp reference $p_\mathrm{T}$ spectra at unmeasured $\sqrt{s}$
A deep neural network interpolates and extrapolates proton-proton reference transverse-momentum spectra to unmeasured center-of-mass energies using ALICE LHC data.