<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "https://jats.nlm.nih.gov/publishing/1.3/JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xml:lang="ru">
  <front xmlns:xlink="http://www.w3.org/1999/xlink">
    <journal-meta>
      <journal-title-group>
        <journal-title>St. Petersburg Polytechnic University Journal: Physics and Mathematics</journal-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Научно-технические ведомости СПбГПУ. Физико-математические науки</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2304-9782, 2618-8686, 2405-7223</issn>
    </journal-meta>
    <article-meta xmlns:xlink="http://www.w3.org/1999/xlink">
      <article-id pub-id-type="publisher-id">15</article-id>
      <article-id pub-id-type="doi">10.18721/JPM.16415</article-id>
      <title-group>
        <article-title>Simulation of semi-inclusive deep inelastic lepton scattering on a proton at energies of 20 – 100 GeV on the basis of the Generative-Adversarial Neural Network</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Моделирование полуинклюзивного, глубоконеупругого рассеяния лептона на протоне при энергиях 20 – 100 ГэВ на основе генеративносостязательной нейронной сети</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-8910-4775</contrib-id>
          <name>
            <surname>Lobanov</surname>
            <given-names>Andrey</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
          <email>lobanov2.aa@edu.spbstu.ru</email>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-0309-5917</contrib-id>
          <name>
            <surname>Berdnikov</surname>
            <given-names>Yaroslav</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
          <email>berdnikov@spbstu.ru</email>
        </contrib>
      </contrib-group>
      <aff id="aff1">Peter the Great St. Petersburg Polytechnic University</aff>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2023-12-31">
        <day>31</day>
        <month>12</month>
        <year>2023</year>
      </pub-date>
      <volume>16</volume>
      <issue>4</issue>
      <fpage>189</fpage>
      <lpage>197</lpage>
      <self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pdf" xlink:href="https://physmath.spbstu.ru/userfiles/files/articles/2023/4/15_189-197_16(3)2023.pdf"/>
      <abstract xml:lang="en">
        <p>This paper continues a series of articles devoted to developing the capabilities of a deep inelastic lepton-proton scattering event generator based on the generative adversarial network (GAN). The investigation has focused on semi-inclusive reactions of deep inelastic scattering and, particularly, on hadron registration. The results confirmed that GAN could accurately generate distributions of physical properties of leptons and hadrons. It worked for different types of leptons and hadrons in the range of initial energies from 20 to 100 GeV in the center-of-mass system. The GAN demonstrated to preserve the inherent correlation between the characteristics of leptons and protons.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>semi-inclusive deep inelastic scattering</kwd>
        <kwd>machine learning</kwd>
        <kwd>neural network</kwd>
        <kwd>generative-adversarial network</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
