Determination of the quark-gluon string parameters from the data on pp, pA and AA collisions at wide energy range using Bayesian Gaussian Process Optimization

TL;DR

This paper applies Bayesian Gaussian Process Optimization to determine parameters of a Monte Carlo model with string fusion, using experimental data from various collision types and energies, to constrain quark-gluon string properties.

Contribution

It introduces a novel application of Bayesian Gaussian Process Optimization to phenomenological models in soft QCD physics, enabling efficient parameter estimation from experimental data.

Findings

Constraints on the transverse radius of the quark-gluon string.

Estimates of the mean multiplicity per rapidity from one string.

Validation of the model against a wide range of collision data.

Abstract

Bayesian Gaussian Process Optimization can be considered as a method of the determination of the model parameters, based on the experimental data. In the range of soft QCD physics, the processes of hadron and nuclear interactions require using phenomenological models containing many parameters. In order to minimize the computation time, the model predictions can be parameterized using Gaussian Process regression, and then provide the input to the Bayesian Optimization. In this paper, the Bayesian Gaussian Process Optimization has been applied to the Monte Carlo model with string fusion. The parameters of the model are determined using experimental data on multiplicity and cross section of pp, pA and AA collisions at wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string ($r_{str}$) and the mean multiplicity per rapidity from one string ($\mu_0$).