Speaker
Andrea Sciandra
(BNL)
Description
The ParticleNet tagger is a graph neural network devoted to the tagging of jets from the hadronization of multiple flavors at the FCC-ee. Its impressive and unprecedented tagging performance allows for accessing rare and challenging hadronic final states. This study shows the fast-simulation-based characterization of the ParticleNet performance evolution as a function of the IDEA vertex detector single-hit resolution, material radiation length and number of layers. Furthermore, an attempt to study impacts in physics applications such as the all-hadronic and Higgs-invisible ZH final states will be shown.
Primary author
Andrea Sciandra
(BNL)
