Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference

TL;DR

This paper explores the use of machine learning to improve constraints on CP violation in the Higgs-top-quark interaction by analyzing full kinematic data from specific Higgs production and decay processes at the LHC.

Contribution

It introduces a machine-learning-based inference method to better constrain CP violation in the Higgs-top interaction using complete kinematic information from collider data.

Findings

Expected exclusion bounds for CP violation at LHC and HL-LHC

Identification of kinematic distributions sensitive to CP violation

Analysis of robustness against theoretical uncertainties

Abstract

While CP violation in the Higgs interactions with massive vector boson is already tightly constrained, the CP nature of the Higgs interactions with fermions is far less constrained. In this work, we assess the potential of machine-learning-based inference methods to constrain CP violation in the Higgs top-Yukawa coupling. This approach enables the use of the full available kinematic information. Concentrating on top-associated Higgs production with the Higgs decaying to two photons, we derive expected exclusion bounds for the LHC and the high-luminosity phase of the LHC. We also study the dependence of these bounds on the Higgs interaction with massive vector bosons and their robustness against theoretical uncertainties. In addition to deriving expected exclusion bounds, we discuss at which level a non-zero CP-violating top-Yukawa coupling can be distinguished from the SM. Moreover, we analyze which kinematic distributions are most sensitive to a CP-violating top-Yukawa coupling.