Bayesian MCMC fitting of a phenomenological model reveals that isospin asymmetry strongly suppresses alpha preformation probability in superheavy nuclei while reproducing shell effects and experimental half-lives.
Bayesian extraction of jet energy loss distributions in heavy-ion collisions
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abstract
Based on the factorization in perturbative QCD, a jet cross sections in heavy-ion collisions can be expressed as a convolution of the jet cross section in $p+p$ collisions and a jet energy loss distribution. Using this simple expression and the Markov Chain Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and $\gamma$-triggered jets in $Pb+Pb$ collisions with different centralities at two colliding energies at the Large Hadron Collider. The average jet energy loss has a dependence on the initial jet energy that is slightly stronger than a logarithmic form and decreases from central to peripheral collisions. The extracted jet energy loss distributions with a scaling behavior in $x=\Delta p_T /\langle \Delta p_T\rangle$ have a large width. These are consistent with the linear Boltzmann transport model simulations, in which the observed jet quenching is caused on the average by only a few out-of-cone scatterings.
fields
nucl-th 2years
2026 2representative citing papers
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.
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Correlation between nuclear isospin asymmetry and $\alpha$-particle preformation probability for superheavy nuclei from a Bayesian inference
Bayesian MCMC fitting of a phenomenological model reveals that isospin asymmetry strongly suppresses alpha preformation probability in superheavy nuclei while reproducing shell effects and experimental half-lives.
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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.