Speaker
Description
The observation of flavor-changing neutral current (FCNC) interactions between the top quark and the Standard Model (SM) Higgs boson would constitute an unambiguous signal of physics beyond the SM. Searches for this process at the LHC are, however, extremely challenging due to the small signal rates and the strong kinematic resemblance between the signal and dominant SM backgrounds, particularly top-quark pair production accompanied by QCD jets. In this talk, I will present a novel deep-learning approach that exploits event-level properties such as QCD color flow through graph-based neural network architectures. By comparing its performance with more traditional methods, including multilayer perceptrons (MLPs), I will demonstrate the potential of these techniques for integration into ATLAS and CMS analysis frameworks, enhancing the sensitivity to this channel.