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

proceedings of xiii quark confinement and the hadron spectrum — pos(confinement2018)

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