Speaker
Description
In extreme environments characterized by very high temperature and density, such as those realized in the early universe and the interior of neutron stars, a state of matter consisting of quarks and gluons, known as the quark-gluon plasma (QGP), is expected to emerge. QGP has been produced in high-energy heavy-ion collisions, enabling extensive studies of its properties. In contrast, whether QGP can be formed in small collision systems remains an open question and has become an important issue in recent high-energy collision experiments.
In this work, we apply Bayesian inference to the Dynamical Core–Corona Initialization model, which simultaneously describes the dynamics of locally equilibrated matter (core), such as QGP, and nonequilibrium matter (corona). We quantitatively investigate fluid formation in proton-proton (pp) collisions. The model parameters controlling fluid formation are constrained using experimental data, and the resulting constraints are compared with those obtained from conventional, non-statistical fits.
The posterior distributions obtained from the Bayesian analysis suggest a larger contribution from the fluid component than that indicated by the conventional fits. We further compare the inferred constraints obtained from different experimental observables to identify observables that are sensitive to fluid formation. In addition, we assess the model's ability to reproduce experimental data using the maximum a posteriori parameters, and discuss the applicability of the model and remaining challenges.
Based on these results, we discuss the possibility of QGP formation in small collision systems and its quantitative assessment through Bayesian inference.