Boosting probes of CP violation in the top Yukawa coupling with Deep Learning

The precise measurement of the top-Higgs coupling is crucial in particle physics, offering insights into potential new physics Beyond the Standard Model (BSM) carrying {\cal CP} Violation (CPV) effects. In this paper, we explore the {\cal CP} properties of a Higgs boson coupling with a top quark pair, focusing on events where the Higgs state decays into a pair of $b$-quarks and the top-antitop system decays leptonically. The novelty of our analysis resides in the exploitation of two conditional Deep Learning (DL) networks: a Multi-Layer Perceptron (MLP) and a Graph Convolution Network (GCN). These models are trained for selected CPV phase values and then used to interpolate all possible values ranging from $0$ to $\pi/2$. This enables a comprehensive assessment of sensitivity across all {\cal CP} phase values, thereby streamlining the process as the models are trained only once. Notably, the conditional GCN exhibits superior performance over the conditional MLP, owing to the nature of graph-based Neural Network (NN) structures. Specifically, for Higgs top coupling modifier set to 1, with $\sqrt{s}= 13.6$ TeV and integrated luminosity of $3$ ab$^{-1}$ GCN excludes the {\cal CP} phase larger than $5^\circ$ at $95.4\%$ Confidence Level (C.L). Our Machine Learning (ML) informed findings indicate that assessment of the {\cal CP} properties of the Higgs coupling to the $t\bar t$ pair can be within reach of the High Luminosity Large Hadron Collider (HL-LHC), quantitatively surpassing the sensitivity of more traditional approaches.