Speaker
Description
The radiation pattern within quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force and for optimizing event generators for particle physics. Jet substructure measurements in electron-proton collisions are of particular interest as many of the complications present at hadron colliders are absent.
In this contribution, a detailed study of jet substructure observables, so-called jet angularities, are presented using data recorded by the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using a novel machine learning technique to correct for detector effects. All of the available reconstructed object information inside a jet is interpreted using a graph neural network and training of these networks was performed using the Perlmutter supercomputer at Berkeley Lab. Results are reported at high transverse momentum transfer Q²>150 GeV², and the analysis is also performed in sub-regions of Q², thus probing scale dependencies of the substructure variables.
PLB 844 (2023) 138101 [arxiv:2303.13620]
