Machine learning models to find unobservable centrality-related parameter values in collisions of different nuclei in the initial energy range from 40 to 200 GeV

Machine learning models to find unobservable centrality-related parameter values in collisions of different nuclei in the initial energy range from 40 to 200 GeV

This paper continues studies in machine learning models capabilities aimed to finding the best way to predict the values of unobservable quantities that characterize centrality, based on experimental data for observable quantities: the number of charged particles and the number of neutrons produced...

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Bibliographic Details
Main Authors: Lobanov Andrey, Berdnikov Alexander, Mitrankova Maria
Format: Article
Language:English
Published: Peter the Great St.Petersburg Polytechnic University 2023-06-01
Series:St. Petersburg Polytechnical University Journal: Physics and Mathematics
Subjects:
machine learning
nuclei collisions
initial energy
r-squared
multilayer perceptron
Online Access:https://physmath.spbstu.ru/article/2023.66.11/

Internet

https://physmath.spbstu.ru/article/2023.66.11/

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