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

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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. / Kovalenko, Vladimir.

в: Proceedings of Science, Том 336, 235, 26.09.2019.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

BibTeX

@article{d8723b39c619478e82b9dfe16e6ba42c,

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).",

keywords = "Gaussian noise (electronic), HADRONS, optimization, Process control, Bayesian Gaussian process, Bayesian optimization, Gaussian process regression, Model parameters, Monte Carlo model, Nuclear interaction, Phenomenological Models, Wide energy range, Gaussian distribution",

author = "Vladimir Kovalenko",

year = "2019",

month = sep,

day = "26",

doi = "10.22323/1.336.0235",

language = "English",

volume = "336",

journal = "Proceedings of Science",

issn = "1824-8039",

publisher = "Sissa Medialab Srl",

note = "13th Quark Confinement and the Hadron Spectrum, Confinement 2018 ; Conference date: 31-07-2018 Through 06-08-2018",

}

RIS

TY - JOUR

T1 - 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

AU - Kovalenko, Vladimir

PY - 2019/9/26

Y1 - 2019/9/26

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).

KW - Gaussian noise (electronic)

KW - HADRONS

KW - optimization

KW - Process control

KW - Bayesian Gaussian process

KW - Bayesian optimization

KW - Gaussian process regression

KW - Model parameters

KW - Monte Carlo model

KW - Nuclear interaction

KW - Phenomenological Models

KW - Wide energy range

KW - Gaussian distribution

UR - http://www.scopus.com/inward/record.url?scp=85073684199&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/determination-quarkgluon-string-parameters-data-pp-pa-aa-collisions-wide-energy-range-using-bayesian

U2 - 10.22323/1.336.0235

DO - 10.22323/1.336.0235

M3 - Conference article

AN - SCOPUS:85073684199

VL - 336

JO - Proceedings of Science

JF - Proceedings of Science

SN - 1824-8039

M1 - 235

T2 - 13th Quark Confinement and the Hadron Spectrum, Confinement 2018

Y2 - 31 July 2018 through 6 August 2018

ER -

ID: 48309823