1. Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA
- Author
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Andreev, V., Arratia, M., Baghdasaryan, A., Baty, A., Begzsuren, K., Bolz, A., Boudry, V., Brandt, G., Britzger, D., Buniatyan, A., Bystritskaya, L., Campbell, A.J., Cantun Avila, K.B., Cerny, K., Chekelian, V., Chen, Z., Contreras, J.G., Cunqueiro Mendez, L., Cvach, J., Dainton, J.B., Daum, K., Deshpande, A., Diaconu, C., Drees, A., Eckerlin, G., Egli, S., Elsen, E., Favart, L., Fedotov, A., Feltesse, J., Fleischer, M., Fomenko, A., Gal, C., Gayler, J., Goerlich, L., Gogitidze, N., Gouzevitch, M., Grab, C., Greenshaw, T., Grindhammer, G., Haidt, D., Henderson, R.C.W., Hessler, J., Hladký, J., Hoffmann, D., Horisberger, R., Hreus, T., Huber, F., Jacobs, P.M., Jacquet, M., Janssen, T., Jung, A.W., Kapichine, M., Katzy, J., Kiesling, C., Klein, M., Kleinwort, C., Klest, H.T., Kogler, R., Kostka, P., Kretzschmar, J., Krücker, D., Krüger, K., Landon, M.P.J., Lange, W., Laycock, P., Lee, S.H., Levonian, S., Li, W., Lin, J., Lipka, K., List, B., List, J., Lobodzinski, B., Long, O.R., Malinovski, E., Martyn, H.-U., Maxfield, S.J., Mehta, A., Meyer, A.B., Meyer, J., Mikocki, S., Mikuni, V.M., Mondal, M.M., Morozov, A., Müller, K., Nachman, B., Naumann, Th., Newman, P.R., Niebuhr, C., Nowak, G., Olsson, J.E., Ozerov, D., Park, S., Pascaud, C., Patel, G.D., Perez, E., Petrukhin, A., Picuric, I., Pitzl, D., Polifka, R., Preins, S., Radescu, V., Raicevic, N., Ravdandorj, T., Reimer, P., Rizvi, E., Robmann, P., Roosen, R., Rostovtsev, A., Rotaru, M., Sankey, D.P.C., Sauter, M., Sauvan, E., Schmitt, S., Schmookler, B.A., Schnell, G., Schoeffel, L., Schöning, A., Sefkow, F., Shushkevich, S., Soloviev, Y., Sopicki, P., South, D., Spaskov, V., Specka, A., Steder, M., Stella, B., Straumann, U., Sun, C., Sykora, T., Thompson, P.D., Acosta, F. Torales, Traynor, D., Tseepeldorj, B., Tu, Z., Tustin, G., Valkárová, A., Vallée, C., Van Mechelen, P., Wegener, D., Wünsch, E., Žáček, J., Zhang, J., Zhang, Z., Žlebčík, R., Zohrabyan, H., Zomer, F., Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Département de Physique des Particules (ex SPP) (DPhP), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut de Physique Nucléaire de Lyon (IPNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and H1
- Subjects
particle, energy ,neural network ,hep-ex ,momentum transfer ,FOS: Physical sciences ,electron p, interaction ,GeV ,High Energy Physics - Experiment ,DESY HERA Stor ,High Energy Physics - Experiment (hep-ex) ,machine learning ,energy, high ,nuclear physics ,network ,strong coupling ,[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] ,structure ,hadron ,higher-dimensional ,Monte Carlo ,transverse momentum, high ,Particle Physics - Experiment ,Berkeley Lab - 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., 30 pages, 10 figures, 8 tables, corrected authorlist
- Published
- 2023
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