Machine-enhanced CP-asymmetries in the Higgs sector

TL;DR

This paper introduces a neural network-based method to construct CP-sensitive observables for Higgs boson studies, significantly enhancing the detection sensitivity of CP-violation effects at the LHC.

Contribution

It presents a novel approach that directly derives CP-sensitive observables from neural network outputs, improving upon traditional angular observables for Higgs CP-violation analysis.

Findings

Neural network-derived observables outperform traditional angular observables in sensitivity.

Kinematic correlations identified by neural networks can inform new angular analysis strategies.

Method enhances the precision of CP-violation measurements in Higgs processes.

Abstract

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.