학술논문
Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA
Document Type
Working Paper
Author
The H1 collaboration; 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.; Avila, K. B. Cantun; Cerny, K.; Chekelian, V.; Chen, Z.; Contreras, J. G.; 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.; 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.; 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.; 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.
Source
PLB 844 (2023) 138101
Subject
Language
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.
Comment: 25 pages, 10 figures, 8 tables, version accepted by Physics Letters B
Comment: 25 pages, 10 figures, 8 tables, version accepted by Physics Letters B