A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton
A neural network, that allows someone to obtain results for semi-inclusive deep inelastic scattering of charged leptons on polarized protons, with the production of pions or strange K mesons, has been developed in this study. The research covered both transverse and longitudinal polarizations of the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Peter the Great St.Petersburg Polytechnic University
2024-03-01
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| Series: | St. Petersburg Polytechnical University Journal: Physics and Mathematics |
| Subjects: | |
| Online Access: | https://physmath.spbstu.ru/article/2024.71.10/ |
| _version_ | 1797216185491652608 |
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| author | Lobanov Andrey Berdnikov Yaroslav Muzyaev Evgeniy |
| author_facet | Lobanov Andrey Berdnikov Yaroslav Muzyaev Evgeniy |
| author_sort | Lobanov Andrey |
| collection | DOAJ |
| description | A neural network, that allows someone to obtain results for semi-inclusive deep inelastic scattering of charged leptons on polarized protons, with the production of pions or strange K mesons, has been developed in this study. The research covered both transverse and longitudinal polarizations of the proton. A range of initial energies of colliding particles was chosen from 20 to 100 GeV in a central mass system. The range is typical for electron-ion colliders currently being designed. It has been shown that it is possible to predict the physical characteristics of the final lepton and hadron with high accuracy as well as different variants of proton polarization using the proposed neural network. |
| first_indexed | 2024-04-24T11:41:57Z |
| format | Article |
| id | doaj.art-b6c58e8244e74001a02d8cd53c696f43 |
| institution | Directory Open Access Journal |
| issn | 2405-7223 |
| language | English |
| last_indexed | 2024-04-24T11:41:57Z |
| publishDate | 2024-03-01 |
| publisher | Peter the Great St.Petersburg Polytechnic University |
| record_format | Article |
| series | St. Petersburg Polytechnical University Journal: Physics and Mathematics |
| spelling | doaj.art-b6c58e8244e74001a02d8cd53c696f432024-04-09T20:08:56ZengPeter the Great St.Petersburg Polytechnic UniversitySt. Petersburg Polytechnical University Journal: Physics and Mathematics2405-72232024-03-0117110.18721/JPM.1711020714726A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized protonLobanov Andrey0https://orcid.org/0000-0002-8910-4775Berdnikov Yaroslav1https://orcid.org/0000-0003-0309-5917Muzyaev Evgeniy2https://orcid.org/0009-0005-7144-4746Peter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityA neural network, that allows someone to obtain results for semi-inclusive deep inelastic scattering of charged leptons on polarized protons, with the production of pions or strange K mesons, has been developed in this study. The research covered both transverse and longitudinal polarizations of the proton. A range of initial energies of colliding particles was chosen from 20 to 100 GeV in a central mass system. The range is typical for electron-ion colliders currently being designed. It has been shown that it is possible to predict the physical characteristics of the final lepton and hadron with high accuracy as well as different variants of proton polarization using the proposed neural network.https://physmath.spbstu.ru/article/2024.71.10/semi-inclusive deep inelastic scatteringasymmetriesmachine learningneural networkgenerative-adversarial network |
| spellingShingle | Lobanov Andrey Berdnikov Yaroslav Muzyaev Evgeniy A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton St. Petersburg Polytechnical University Journal: Physics and Mathematics semi-inclusive deep inelastic scattering asymmetries machine learning neural network generative-adversarial network |
| title | A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton |
| title_full | A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton |
| title_fullStr | A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton |
| title_full_unstemmed | A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton |
| title_short | A generative adversarial network as the basis for a semi-inclusive deep inelastic lepton scattering generator on a polarized proton |
| title_sort | generative adversarial network as the basis for a semi inclusive deep inelastic lepton scattering generator on a polarized proton |
| topic | semi-inclusive deep inelastic scattering asymmetries machine learning neural network generative-adversarial network |
| url | https://physmath.spbstu.ru/article/2024.71.10/ |
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