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 Online-Ressource | |
| Verfasst von: | Andreev, Vladimir [VerfasserIn]  |
| Schöning, André [VerfasserIn] | |
| Titel: | Unbinned deep learning jet substructure measurement in high Q2 ep collisions at HERA |
| Verf.angabe: | V. Andreev, M. Arratia, A. Baghdasaryan, A. Baty, K. Begzsuren, A. Bolz, V. Boudry, G. Brandt, D. Britzger, A. Buniatyan, L. Bystritskaya, A.J. Campbell, K.B. Cantun Avila, K. Cerny, V. Chekelian, Z. Chen, J.G. Contreras, J. Cvach, J.B. Dainton, K. Daum, A. Deshpande, C. Diaconu, A. Drees, G. Eckerlin, S. Egli, E. Elsen, L. Favart, A. Fedotov, J. Feltesse, M. Fleischer, A. Fomenko, C. Gal, J. Gayler, L. Goerlich, N. Gogitidze, M. Gouzevitch, C. Grab, T. Greenshaw, G. Grindhammer, D. Haidt, R.C.W. Henderson, J. Hessler, J. Hladký, D. Hoffmann, R. Horisberger, T. Hreus, F. Huber, P.M. Jacobs, M. Jacquet, T. Janssen, A.W. Jung, J. Katzy, C. Kiesling, M. Klein, C. Kleinwort, H.T. Klest, R. Kogler, P. Kostka, J. Kretzschmar, D. Krücker, K. Krüger, M.P.J. Landon, W. Lange, P. Laycock, S.H. Lee, S. Levonian, W. Li, J. Lin, K. Lipka, B. List, J. List, B. Lobodzinski, O.R. Long, E. Malinovski, H.-U. Martyn, S.J. Maxfield, A. Mehta, A.B. Meyer, J. Meyer, S. Mikocki, V.M. Mikuni, M.M. Mondal, K. Müller, B. Nachman, Th. Naumann, P.R. Newman, C. Niebuhr, G. Nowak, J.E. Olsson, D. Ozerov, S. Park, C. Pascaud, G.D. Patel, E. Perez, A. Petrukhin, I. Picuric, D. Pitzl, R. Polifka, S. Preins, V. Radescu, N. Raicevic, T. Ravdandorj, P. Reimer, E. Rizvi, P. Robmann, R. Roosen, A. Rostovtsev, M. Rotaru, D.P.C. Sankey, M. Sauter, E. Sauvan, S. Schmitt, B.A. Schmookler, G. Schnell, L. Schoeffel, A. Schöning, F. Sefkow, S. Shushkevich, Y. Soloviev, P. Sopicki, D. South, A. Specka, M. Steder, B. Stella, U. Straumann, C. Sun, T. Sykora, P.D. Thompson, F. Torales Acosta, D. Traynor, B. Tseepeldorj, Z. Tu, G. Tustin, A. Valkárová, C. Vallée, P. Van Mechelen, D. Wegener, E. Wünsch, J. Žáček, J. Zhang, Z. Zhang, R. Žlebčík, H. Zohrabyan, F. Zomer |
| E-Jahr: | 2023 |
| Jahr: | 10 September 2023 |
| Umfang: | 21 S. |
| Fussnoten: | Online veröffentlicht: 31. Juli 2023, Artikelversion: 14. August 2023 ; Gesehen am 13.12.2023 ; Im Titel ist die Zahl 2 hochgestellt |
| Titel Quelle: | Enthalten in: Physics letters / B |
| Ort Quelle: | Amsterdam : North-Holland Publ., 1967 |
| Jahr Quelle: | 2023 |
| Band/Heft Quelle: | 844(2023) vom: Sept., Artikel-ID 138101, Seite 1-21 |
| ISSN Quelle: | 1873-2445 |
| 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 kT jet clustering algorithm. Results are reported at high transverse momentum transfer Q2>150GeV2, and inelasticity 0.2<y<0.7. The analysis is also performed in sub-regions of Q2, 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. |
| DOI: | doi:10.1016/j.physletb.2023.138101 |
| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt. kostenfrei: Volltext: https://doi.org/10.1016/j.physletb.2023.138101 |
| kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S0370269323004355 | |
| DOI: https://doi.org/10.1016/j.physletb.2023.138101 | |
| Datenträger: | Online-Ressource |
| Sprache: | eng |
| K10plus-PPN: | 1874363943 |
| Verknüpfungen: | → Zeitschrift |
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