Boosting probes of $$ \mathcal{CP} $$ violation in the top Yukawa coupling with Deep Learning

A bstract 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 $$ \mathcal{CP} $$ CP Violation (CPV) effects. In this paper, we explore the $$ \mathcal{CP} $$ 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 π /2. This enables a comprehensive assessment of sensitivity across all $$ \mathcal{CP} $$ 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 $$ \sqrt{s} $$ s = 13.6 TeV and integrated luminosity of 3 ab −1 GCN excludes the $$ \mathcal{CP} $$ CP phase larger than 5 ° at 95 . 4% Confidence Level (C.L). Our Machine Learning (ML) informed findings indicate that assessment of the $$ \mathcal{CP} $$ CP properties of the Higgs coupling to the $$ t\overline{t} $$ t t ¯ pair can be within reach of the High Luminosity Large Hadron Collider (HL-LHC), quantitatively surpassing the sensitivity of more traditional approaches.