Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA

Andreev, V.; Vallée, C.; Daum, K.; Gouzevitch, M.; Baghdasaryan, A.; Britzger, D.; Patel, G. D.; Traynor, D.; Chekelian, V.; Žlebčík, R.; H1 Collaboration; Laycock, P.; Mehta, A.; Egli, S.; Picuric, I.; Wünsch, E.; Park, S.; Olsson, J. E.; Schmookler, B. A.; Maxfield, S. J.; Baty, A.; Hreus, T.; Sun, C.; Huber, F.; Kapichine, M.; Cerny, K.; Schöning, A.; Lipka, K.; Spaskov, V.; Valkárová, A.; Petrukhin, A.; Zohrabyan, H.; Favart, L.; Henderson, R. C. W.; Jung, H.; Li, W.; Katzy, J.; Rizvi, E.; Van Mechelen, P.; Campbell, A. J.; Polifka, R.; Jacquet, M.; Fleischer, M.; Malinovski, E.; Lobodzinski, B.; Hoffmann, D.; Martyn, H.-U.; Steder, M.; Eckerlin, G.; Contreras, J. G.; Roosen, R.; Krücker, D.; Begzsuren, K.; Nachman, B.; Mikuni, V. M.; Wegener, D.; Elsen, E.; Janssen, T.; Sauvan, E.; Perez, E.; Tu, Z.; Gayler, J.; Kleinwort, C.; Arratia, M.; Ravdandorj, T.; Jung, A. W.; Newman, P. R.; Lin, J.; Goerlich, L.; Feltesse, J.; Brandt, G.; Shushkevich, S.; Fomenko, A.; Hladký, J.; Pitzl, D.; Lee, S. H.; Krüger, K.; List, B.; Jacobs, P. M.; Bolz, A.; Meyer, J.; Mondal, M. M.; Radescu, V.; Gal, C.; Levonian, S.; List, J.; Grindhammer, G.; Cunqueiro Mendez, L.; Thompson, P. D.; Greenshaw, T.; Tseepeldorj, B.; Buniatyan, A.; Kostka, P.; Reimer, P.; Kretzschmar, J.; Chen, Z.; Soloviev, Y.; Zhang, Z.; Diaconu, C.; Schmitt, S.; Nowak, G.; Sankey, D. P. C.; Klest, H. T.; Ozerov, D.; Müller, K.; Boudry, V.; Morozov, A.; Raicevic, N.; Landon, M. P. J.; Žáček, J.; Preins, S.; Rostovtsev, A.; Dainton, J. B.; Straumann, U.; Pascaud, C.; Fedotov, A.; Schoeffel, L.; Sauter, M.; Gogitidze, N.; South, D.; Grab, C.; Bystritskaya, L.; Long, O. R.; Mikocki, S.; Naumann, Th.; Sykora, T.; Zhang, J.; Kogler, R.; Zomer, F.; Cvach, J.; Sopicki, P.; Meyer, A. B.; Haidt, D.; Lange, W.; Deshpande, A.; Sefkow, F.; Horisberger, R.; Stella, B.; Rotaru, M.; Cantun Avila, K. B.; Klein, M.; Niebuhr, C.; Specka, A.; Kiesling, C.; Robmann, P.

%0 Electronic Article
%A Andreev, V.
%A Arratia, M.
%A Baghdasaryan, A.
%A Baty, A.
%A Begzsuren, K.
%A Bolz, A.
%A Boudry, V.
%A Brandt, G.
%A Britzger, D.
%A Buniatyan, A.
%A Bystritskaya, L.
%A Campbell, A. J.
%A Cantun Avila, K. B.
%A Cerny, K.
%A Chekelian, V.
%A Chen, Z.
%A Contreras, J. G.
%A Cunqueiro Mendez, L.
%A Cvach, J.
%A Dainton, J. B.
%A Daum, K.
%A Deshpande, A.
%A Diaconu, C.
%A Eckerlin, G.
%A Egli, S.
%A Elsen, E.
%A Favart, L.
%A Fedotov, A.
%A Feltesse, J.
%A Fleischer, M.
%A Fomenko, A.
%A Gal, C.
%A Gayler, J.
%A Goerlich, L.
%A Gogitidze, N.
%A Gouzevitch, M.
%A Grab, C.
%A Greenshaw, T.
%A Grindhammer, G.
%A Haidt, D.
%A Henderson, R. C. W.
%A Hladký, J.
%A Hoffmann, D.
%A Horisberger, R.
%A Hreus, T.
%A Huber, F.
%A Jacobs, P. M.
%A Jacquet, M.
%A Janssen, T.
%A Jung, A. W.
%A Jung, H.
%A Kapichine, M.
%A Katzy, J.
%A Kiesling, C.
%A Klein, M.
%A Kleinwort, C.
%A Klest, H. T.
%A Kogler, R.
%A Kostka, P.
%A Kretzschmar, J.
%A Krücker, D.
%A Krüger, K.
%A Landon, M. P. J.
%A Lange, W.
%A Laycock, P.
%A Lee, S. H.
%A Levonian, S.
%A Lin, J.
%A Lipka, K.
%A List, B.
%A List, J.
%A Li, W.
%A Lobodzinski, B.
%A Long, O. R.
%A Malinovski, E.
%A Martyn, H.-U.
%A Maxfield, S. J.
%A Mehta, A.
%A Meyer, A. B.
%A Meyer, J.
%A Mikocki, S.
%A Mikuni, V. M.
%A Mondal, M. M.
%A Morozov, A.
%A Müller, K.
%A Nachman, B.
%A Naumann, Th.
%A Newman, P. R.
%A Niebuhr, C.
%A Nowak, G.
%A Olsson, J. E.
%A Ozerov, D.
%A Park, S.
%A Pascaud, C.
%A Patel, G. D.
%A Perez, E.
%A Petrukhin, A.
%A Picuric, I.
%A Pitzl, D.
%A Polifka, R.
%A Preins, S.
%A Radescu, V.
%A Raicevic, N.
%A Ravdandorj, T.
%A Reimer, P.
%A Rizvi, E.
%A Robmann, P.
%A Roosen, R.
%A Rostovtsev, A.
%A Rotaru, M.
%A Sankey, D. P. C.
%A Sauter, M.
%A Sauvan, E.
%A Schmitt, S.
%A Schmookler, B. A.
%A Schoeffel, L.
%A Schöning, A.
%A Sefkow, F.
%A Shushkevich, S.
%A Soloviev, Y.
%A Sopicki, P.
%A South, D.
%A Spaskov, V.
%A Specka, A.
%A Steder, M.
%A Stella, B.
%A Straumann, U.
%A Sun, C.
%A Sykora, T.
%A Thompson, P. D.
%A Traynor, D.
%A Tseepeldorj, B.
%A Tu, Z.
%A Valkárová, A.
%A Vallée, C.
%A Van Mechelen, P.
%A Žáček, J.
%A Žlebčík, R.
%A Wegener, D.
%A Wünsch, E.
%A Zhang, J.
%A Zhang, Z.
%A Zohrabyan, H.
%A Zomer, F.
%T Unbinned Deep Learning Jet Substructure Measurement in High Q<sup>2</sup> ep collisions at HERA
%N DESY-23-034
%M PUBDB-2023-01338
%M DESY-23-034
%M arXiv:2303.13620
%D 2023
%Z To be submitted to Nature Physics
%X 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<sub>T</sub> jet clustering algorithm. Results are reported at high transverse momentum transfer Q<sup>2</sup> > 150 GeV<sup>2</sup>, and inelasticity 0.2  <  y  <  0.7. The analysis is also performed in sub-regions of Q<sup>2</sup>, 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.
%K energy, high (INSPIRE)
%K electron p, interaction (INSPIRE)
%K particle, energy (INSPIRE)
%K transverse momentum, high (INSPIRE)
%K structure (INSPIRE)
%K DESY HERA Stor (INSPIRE)
%K nuclear physics (INSPIRE)
%K GeV (INSPIRE)
%K higher-dimensional (INSPIRE)
%K Berkeley Lab (INSPIRE)
%K machine learning (INSPIRE)
%K momentum transfer (INSPIRE)
%K network (INSPIRE)
%K neural network (INSPIRE)
%K strong coupling (INSPIRE)
%K hadron (INSPIRE)
%K Monte Carlo (INSPIRE)
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.3204/PUBDB-2023-01338
%U https://bib-pubdb1.desy.de/record/580603

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