Tax regulatıon of the self-employed in Russian Law

Kovalenko, Kseniya E.; Kovalenko, Eugene V.; Kovalenko, Nataliya E.

doi:10.18468/estcien.2019v9n2.p45-53

Cited by 1 publication

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“…The present model [6,7,8] is based on the partonic picture of nucleon collisions. Each nucleon is supposed to consist of a valence quark-diquark pair and a certain number of sea quark-antiquark pairs (see Fig.…”

Section: Monte Carlo Model Descriptionmentioning

confidence: 99%

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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

Kovalenko¹

2019

Proceedings of XIII Quark Confinement and the Hadron Spectrum — PoS(Confinement2018)

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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 (rstr) and the mean multiplicity per rapidity from one string (μ0).

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Section: Monte Carlo Model Descriptionmentioning

confidence: 99%

“…In this paper, the Gaussian process was applied to the Monte Carlo model with string fusion [6,7,8] to optimize a number of parameters based on multiplicity data in pp, p-Pb and Pb-Pb collisions at LHC energies. This model enables to describe collisions with protons and nuclei on the partonic level.…”

Section: Introductionmentioning

confidence: 99%

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

Kovalenko¹

2019

Proceedings of XIII Quark Confinement and the Hadron Spectrum — PoS(Confinement2018)

View full text Add to dashboard Cite

show abstract

Tax regulatıon of the self-employed in Russian Law

Cited by 1 publication

References 0 publications

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

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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