Machine-enhanced CP-asymmetries in the Higgs sector

Improving the sensitivity to CP-violation in the Higgs sector is one of the pillars of the precision Higgs programme at the Large Hadron Collider. We present a simple method that allows CP-sensitive observables to be directly constructed from the output of neural networks. We show that these observables have improved sensitivity to CP-violating effects in the production and decay of the Higgs boson, when compared to the use of traditional angular observables alone. The kinematic correlations identified by the neural networks can be used to design new analyses based on angular observables, with a similar improvement in sensitivity. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP(3).

Automated summary

Improving the sensitivity to CP-violation in the Higgs sector is crucial for the precision Higgs programme at the Large Hadron Collider. Researchers have developed a simple method that directly constructs CP-sensitive observables from neural network outputs, which demonstrates improved sensitivity to CP-violating effects in the production and decay of the Higgs boson compared to traditional angular observables. The kinematic correlations identified by the neural networks can also be utilized to design new analyses based on angular observables, leading to a similar improvement in sensitivity.