Unbinned deep learning jet substructure measurement in high Q.sup.2./sup. ep collisions at HERA

0582507 - FZÚ 2024 RIV NL eng J - Článek v odborném periodiku
Andreev, V. - Arratia, M. - Baghdasaryan, A. - Cvach, Jaroslav - Hladký, Jan - Reimer, Petr … celkem 144 autorů
Unbinned deep learning jet substructure measurement in high Q² ep collisions at HERA.
Physics Letters. B. Roč. 844, Sept (2023), č. článku 138101. ISSN 0370-2693. E-ISSN 1873-2445
Institucionální podpora: RVO:68378271
Klíčová slova: DESY HERA Stor * H1 * nuclear physics * quark: jet
Obor OECD: Particles and field physics
Impakt faktor: 4.3, rok: 2022
Způsob publikování: Open access

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.
Trvalý link: https://hdl.handle.net/11104/0350569