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@ARTICLE{Andreev:580603,
      author       = {Andreev, V. and Arratia, M. and Baghdasaryan, A. and Baty,
                      A. and Begzsuren, K. and Bolz, A. and Boudry, V. and Brandt,
                      G. and Britzger, D. and Buniatyan, A. and Bystritskaya, L.
                      and Campbell, A. J. and Cantun Avila, K. B. and Cerny, K.
                      and Chekelian, V. and Chen, Z. and Contreras, J. G. and
                      Cunqueiro Mendez, L. and Cvach, J. and Dainton, J. B. and
                      Daum, K. and Deshpande, A. and Diaconu, C. and Eckerlin, G.
                      and Egli, S. and Elsen, E. and Favart, L. and Fedotov, A.
                      and Feltesse, J. and Fleischer, M. and Fomenko, A. and Gal,
                      C. and Gayler, J. and Goerlich, L. and Gogitidze, N. and
                      Gouzevitch, M. and Grab, C. and Greenshaw, T. and
                      Grindhammer, G. and Haidt, D. and Henderson, R. C. W. and
                      Hladký, J. and Hoffmann, D. and Horisberger, R. and Hreus,
                      T. and Huber, F. and Jacobs, P. M. and Jacquet, M. and
                      Janssen, T. and Jung, A. W. and Jung, H. and Kapichine, M.
                      and Katzy, J. and Kiesling, C. and Klein, M. and Kleinwort,
                      C. and Klest, H. T. and Kogler, R. and Kostka, P. and
                      Kretzschmar, J. and Krücker, D. and Krüger, K. and Landon,
                      M. P. J. and Lange, W. and Laycock, P. and Lee, S. H. and
                      Levonian, S. and Lin, J. and Lipka, K. and List, B. and
                      List, J. and Li, W. and Lobodzinski, B. and Long, O. R. and
                      Malinovski, E. and Martyn, H.-U. and Maxfield, S. J. and
                      Mehta, A. and Meyer, A. B. and Meyer, J. and Mikocki, S. and
                      Mikuni, V. M. and Mondal, M. M. and Morozov, A. and Müller,
                      K. and Nachman, B. and Naumann, Th. and Newman, P. R. and
                      Niebuhr, C. and Nowak, G. and Olsson, J. E. and Ozerov, D.
                      and Park, S. and Pascaud, C. and Patel, G. D. and Perez, E.
                      and Petrukhin, A. and Picuric, I. and Pitzl, D. and Polifka,
                      R. and Preins, S. and Radescu, V. and Raicevic, N. and
                      Ravdandorj, T. and Reimer, P. and Rizvi, E. and Robmann, P.
                      and Roosen, R. and Rostovtsev, A. and Rotaru, M. and Sankey,
                      D. P. C. and Sauter, M. and Sauvan, E. and Schmitt, S. and
                      Schmookler, B. A. and Schoeffel, L. and Schöning, A. and
                      Sefkow, F. and Shushkevich, S. and Soloviev, Y. and Sopicki,
                      P. and South, D. and Spaskov, V. and Specka, A. and Steder,
                      M. and Stella, B. and Straumann, U. and Sun, C. and Sykora,
                      T. and Thompson, P. D. and Traynor, D. and Tseepeldorj, B.
                      and Tu, Z. and Valkárová, A. and Vallée, C. and Van
                      Mechelen, P. and Žáček, J. and Žlebčík, R. and
                      Wegener, D. and Wünsch, E. and Zhang, J. and Zhang, Z. and
                      Zohrabyan, H. and Zomer, F.},
      collaboration = {{H1 Collaboration}},
      title        = {{U}nbinned {D}eep {L}earning {J}et {S}ubstructure
                      {M}easurement in {H}igh ${Q}^2$ ep collisions at {HERA}},
      reportid     = {PUBDB-2023-01338, DESY-23-034. arXiv:2303.13620},
      year         = {2023},
      note         = {To be submitted to Nature Physics},
      abstract     = {The radiation pattern within high energy quark- and
                      gluon-initiated jets (jet substructure) is used extensively
                      as a precision probe of the strong force as well as an
                      environment for optimizing event generators with numerous
                      applications in high energy particle and nuclear physics.
                      Looking at electron-proton collisions is of particular
                      interest as many of the complications present at hadron
                      colliders are absent. A detailed study of modern jet
                      substructure observables, jet angularities, in
                      electron-proton collisions is presented using data recorded
                      using the H1 detector at HERA. The measurement is unbinned
                      and multi-dimensional, using machine learning to correct for
                      detector effects. All of the available reconstructed object
                      information of the respective jets is interpreted by a graph
                      neural network, achieving superior precision on a selected
                      set of jet angularities. Training these networks was enabled
                      by the use of a large number of GPUs in the Perlmutter
                      supercomputer at Berkeley Lab. The particle jets are
                      reconstructed in the laboratory frame, using the
                      $k_{\mathrm{T}}$ jet clustering algorithm. Results are
                      reported at high transverse momentum transfer $Q^2>150$
                      GeV${}^2$, and inelasticity $0.2 < y < 0.7$. The analysis is
                      also performed in sub-regions of $Q^2$, thus probing scale
                      dependencies of the substructure variables. The data are
                      compared with a variety of predictions and point towards
                      possible improvements of such models.},
      keywords     = {energy, high (INSPIRE) / electron p, interaction (INSPIRE)
                      / particle, energy (INSPIRE) / transverse momentum, high
                      (INSPIRE) / structure (INSPIRE) / DESY HERA Stor (INSPIRE) /
                      nuclear physics (INSPIRE) / GeV (INSPIRE) /
                      higher-dimensional (INSPIRE) / Berkeley Lab (INSPIRE) /
                      machine learning (INSPIRE) / momentum transfer (INSPIRE) /
                      network (INSPIRE) / neural network (INSPIRE) / strong
                      coupling (INSPIRE) / hadron (INSPIRE) / Monte Carlo
                      (INSPIRE)},
      cin          = {H1},
      cid          = {I:(DE-H253)H1-20120806},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {EXP:(DE-588)4443767-5},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2303.13620},
      howpublished = {arXiv:2303.13620},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2303.13620;\%\%$},
      doi          = {10.3204/PUBDB-2023-01338},
      url          = {https://bib-pubdb1.desy.de/record/580603},
}