Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA
Creators: name: Andreev, V.
name: Arratia, M.
name: Baghdasaryan, A.
name: Baty, A.
name: Begzsuren, K.
name: Bolz, A.
name: Boudry, V.
name: Brandt, G.
name: Britzger, D.
name: Buniatyan, A.
name: Bystritskaya, L.
name: Campbell, A. J.
name: Cantun Avila, K. B.
name: Cerny, K.
name: Chekelian, V.
name: Chen, Z.
name: Contreras, J. G.
name: Cunqueiro Mendez, L.
name: Cvach, J.
name: Dainton, J. B.
name: Daum, K.
name: Deshpande, A.
name: Diaconu, C.
name: Eckerlin, G.
name: Egli, S.
name: Elsen, E.
name: Favart, L.
name: Fedotov, A.
name: Feltesse, J.
name: Fleischer, M.
name: Fomenko, A.
name: Gal, C.
name: Gayler, J.
name: Goerlich, L.
name: Gogitidze, N.
name: Gouzevitch, M.
name: Grab, C.
name: Greenshaw, T.
name: Grindhammer, G.
name: Haidt, D.
name: Henderson, R. C. W.
name: Hladký, J.
name: Hoffmann, D.
name: Horisberger, R.
name: Hreus, T.
name: Huber, F.
name: Jacobs, P. M.
name: Jacquet, M.
name: Janssen, T.
name: Jung, A. W.
name: Jung, H.
name: Kapichine, M.
name: Katzy, J.
name: Kiesling, C.
name: Klein, M.
name: Kleinwort, C.
name: Klest, H. T.
name: Kogler, R.
name: Kostka, P.
name: Kretzschmar, J.
name: Krücker, D.
name: Krüger, K.
name: Landon, M. P. J.
name: Lange, W.
name: Laycock, P.
name: Lee, S. H.
name: Levonian, S.
name: Lin, J.
name: Lipka, K.
name: List, B.
name: List, J.
name: Li, W.
name: Lobodzinski, B.
name: Long, O. R.
name: Malinovski, E.
name: Martyn, H.-U.
name: Maxfield, S. J.
name: Mehta, A.
name: Meyer, A. B.
name: Meyer, J.
name: Mikocki, S.
name: Mikuni, V. M.
name: Mondal, M. M.
name: Morozov, A.
name: Müller, K.
name: Nachman, B.
name: Naumann, Th.
name: Newman, P. R.
name: Niebuhr, C.
name: Nowak, G.
name: Olsson, J. E.
name: Ozerov, D.
name: Park, S.
name: Pascaud, C.
name: Patel, G. D.
name: Perez, E.
name: Petrukhin, A.
name: Picuric, I.
name: Pitzl, D.
name: Polifka, R.
name: Preins, S.
name: Radescu, V.
name: Raicevic, N.
name: Ravdandorj, T.
name: Reimer, P.
name: Rizvi, E.
name: Robmann, P.
name: Roosen, R.
name: Rostovtsev, A.
name: Rotaru, M.
name: Sankey, D. P. C.
name: Sauter, M.
name: Sauvan, E.
name: Schmitt, S.
name: Schmookler, B. A.
name: Schoeffel, L.
name: Schöning, A.
name: Sefkow, F.
name: Shushkevich, S.
name: Soloviev, Y.
name: Sopicki, P.
name: South, D.
name: Spaskov, V.
name: Specka, A.
name: Steder, M.
name: Stella, B.
name: Straumann, U.
name: Sun, C.
name: Sykora, T.
name: Thompson, P. D.
name: Traynor, D.
name: Tseepeldorj, B.
name: Tu, Z.
name: Valkárová, A.
name: Vallée, C.
name: Van Mechelen, P.
name: Žáček, J.
name: Žlebčík, R.
name: Wegener, D.
name: Wünsch, E.
name: Zhang, J.
name: Zhang, Z.
name: Zohrabyan, H.
name: Zomer, F.
name: H1 Collaboration
Publisher: name: Deutsches Elektronen-Synchrotron, DESY, Hamburg
Year: 2023
Subjects: energy, high
electron p, interaction
particle, energy
transverse momentum, high
structure
DESY HERA Stor
nuclear physics
GeV
higher-dimensional
Berkeley Lab
machine learning
momentum transfer
network
neural network
strong coupling
hadron
Monte Carlo

description: 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 $Q^2&gt;150$ GeV${}^2$, and inelasticity $0.2 &lt; y &lt; 0.7$. 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.