Reassessing CP Violation in the C2HDM with Machine Learning

TL;DR

This paper uses machine learning to explore the parameter space of the complex 2-Higgs Doublet Model, focusing on CP violation signals in the Higgs boson and their compatibility with experimental constraints.

Contribution

It introduces ML techniques combined with kite diagrams to analyze CP violation in the C2HDM, revealing potential large CP-odd couplings consistent with current data.

Findings

Kite diagrams are crucial for accurate analysis.

ML techniques help identify viable CP-violating parameter regions.

Large fermion CP-odd couplings remain possible within experimental bounds.

Abstract

We provide a study of the parameter space of the complex 2-Higgs Doublet Model (C2HDM), focusing on signs of large CP-violating couplings of the 125 GeV Higgs boson with the fermions. The study is performed utilizing Machine Learning (ML) techniques developed recently for parameter space exploration, including an Evolutionary Strategy Algorithm and Novelty Reward. We give particular attention to the electron electric dipole moment (eEDM). We confirm that the recently found kite diagrams are crucial for the outcome of the analysis. Moreover, their use also mitigates the dependence of the results on the scale and scheme choice of the masses in the loop diagrams. We furthermore point out that, already at the current level of experimental precision, the Barr-Zee diagrams with charm quark loops must be taken into account. The combined use of kite diagrams and ML techniques allows for the resurrection of large fermion CP-odd couplings for Type-II and Flipped C2HDM when the 125 GeV Higgs coincides with the second lightest neutral scalar. This arises due to cancellations, typically of the per-mil order, which, moreover, will still be possible for a foreseeable eEDM precision down to $10^{-33}$ e.cm. For these cases, the constraints on the CP-odd couplings arises from the precision LHC measurements.