Arianna Garcia Caffaro

Since the Higgs boson’s discovery in 2012, the collider physics community has centered its efforts on thoroughly studying this particle’s properties. As a step towards that goal, the ATLAS Collaboration has undertaken a study of the CP nature of the Higgs Yukawa coupling to the tau lepton.  An analysis with LHC Run 2 data has measured the CP mixing angle to be 9 ± 16°, excluding the pure CP-odd Higgs hypothesis at 3.4 sigma.  The next iteration of this analysis with partial Run 3 data is underway.  Building on earlier analysis techniques, a new machine learning-based method for measuring the CP mixing angle, which achieves a preliminary ~40% sensitivity improvement, will be discussed.  Furthermore, in view of the rapidly increasing interest in jet substructure measurements, a study on the robustness of Neural Networks (NNs) commonly used for jet tagging will be presented.  As a step towards better understanding the role QCD systematics play in Machine Learning applications, this study proposes new metrics to quantify the NNs’ robustness against non-perturbative uncertainties originating from the hadronization process.