Publication
Title
Unbinned deep learning jet substructure measurement in high Q² ep collisions at HERA
Author
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 electronproton collisions is of particular interest as many of the complications present at hadron colliders are absent. Adetailed 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 kTjet clustering algorithm. Results are reported at high transverse momentum transfer Q(2)> 150GeV(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. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
Language
English
Source (journal)
Physics letters : B. - Amsterdam, 1967, currens
Publication
Amsterdam : North-Holland , 2023
ISSN
0370-2693
DOI
10.1016/J.PHYSLETB.2023.138101
Volume/pages
844 (2023) , p. 1-21
Article Reference
138101
ISI
001068892700001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
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Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 30.10.2023
Last edited 10.11.2023
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