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

Research output

Abstract

Bayesian Gaussian Process Optimization can be considered as a method for the determination of the model parameters, based on 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 in a wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string (rstr) and the mean multiplicity per rapidity from one string (μ0).

Original languageEnglish
Article number235
JournalProceedings of Science
Volume336
DOIs
Publication statusPublished - 26 Sep 2019
Event13th Quark Confinement and the Hadron Spectrum, Confinement 2018 - Maynooth
Duration: 31 Jul 20186 Aug 2018

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strings
quarks
optimization
collisions
energy
nuclear interactions
regression analysis
quantum chromodynamics
fusion
physics
radii
cross sections
predictions
interactions

Scopus subject areas

  • General
  • Nuclear and High Energy Physics

Cite this

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title = "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",
abstract = "Bayesian Gaussian Process Optimization can be considered as a method for the determination of the model parameters, based on 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 in a wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string (rstr) and the mean multiplicity per rapidity from one string (μ0).",
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AU - Kovalenko, Vladimir

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N2 - Bayesian Gaussian Process Optimization can be considered as a method for the determination of the model parameters, based on 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 in a wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string (rstr) and the mean multiplicity per rapidity from one string (μ0).

AB - Bayesian Gaussian Process Optimization can be considered as a method for the determination of the model parameters, based on 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 in a wide energy range. The results provide important constraints on the transverse radius of the quark-gluon string (rstr) and the mean multiplicity per rapidity from one string (μ0).

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