Speaker: Shu Tian Eu, Physics PhD Graduate Student
Abstract: The origin of the masses and mixing angles of quarks and leptons remains one of the most intriguing problems to explore in particle physics. In the quark sector, we studied a minimal flavored gauge-mediated supersymmetry breaking model in which the Higgs and messenger doublets are embedded in multiplets of the S3 symmetry. This discrete non-Abelian symmetry correlates the flavor structure of the quark and lepton Yukawa couplings with the structure of the messenger Yukawa couplings that contribute to the soft supersymmetry breaking mass parameters. In the lepton sector, we focused our studies in a class of leptonic portal matter models with an additional U(1) symmetry, as motivated by sub-GeV dark matter models. We presented a minimal construction of such leptonic model that address the muon g-2 anomaly, and we provided a more complete theory based on a SU(2) x SU(2) dark gauge group. We also employed new machine learning techniques, namely reinforcement learning, to develop a machine learning model builder to explore this framework. We successfully trained a reinforcement learning agent to explore this model space and found new possible portal matter models that are consistent with experimental constraints including the anomalous muon magnetic moment.