# Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC

## TL;DR

This paper introduces a graph neural network approach to determine the CP nature of the top-Higgs coupling at the LHC, achieving effective discrimination between CP-even and CP-odd interactions with realistic data volumes.

## Contribution

The study applies message passing neural networks to particle physics data, providing a novel method for probing the CP properties of the top-Higgs interaction.

## Key findings

- Effective CP discrimination at the LHC with 300 fb$^{-1}$ data
- Uses semi-leptonic top-Higgs decay channel
- Achieves high classification accuracy

## Abstract

The top-Higgs coupling plays an important role in particle physics and cosmology. The precision measurements of this coupling can provide an insight to new physics beyond the Standard Model. In this paper, we propose to use Message Passing Neural Network (MPNN) to reveal the CP nature of top-Higgs interaction through semi-leptonic channel $pp \to t(\to b\ell^-\nu_\ell)\bar{t}(\to \bar{b}jj)h(\to b\bar{b})$. Using the test statistics constructed from the event classification probabilities given by the MPNN, we find that the pure CP-even and CP-odd components can be well distinguished at the LHC, with at most 300 fb$^{-1}$ experimental data.