Search

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 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. [ABSTRACT FROM AUTHOR]