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

Abstract: 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 CP Violation (CPV) effects. In this paper, we explore the 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 $-\frac{\pi}{2} \text{ to } \frac{\pi}{2}$. This enables a comprehensive assessment of sensitivity across all 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 CP phase larger than $|5^\circ|$ at $95.4\%$ Confidence Level (C.L). Our Machine Learning (ML) informed findings indicate that assessment of the CP properties of the Higgs coupling to the $t\bar t$ pair can be within reach of the HL-LHC, quantitatively surpassing the sensitivity of more traditional approaches.