Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs - W. Esmail; A. Hammad; M. Nojiri; Christiane Scherb (Journal of High Energy Physics)
Abstract In this paper we explore the possibility of utilizing Deep Learning in measuring the CP properties of the coupling of Higgs boson to τ leptons at the High Luminosity Large Hadron Collider. We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. The angle between τ lepton decay planes at the detector level is CP-sensitive observables, and we develop Heterogeneous Graphs that integrate diverse node and edge structures to incorporate the CP-sensitive observable efficiently. Using simplified detector simulations we estimate the reconstruction accuracy of the angle between τ lepton planes at the detector level, considering hadronic τ decay modes and standard model backgrounds. With s $$ \sqrt{s} $$ = 14 TeV and L $$ \mathcal{L} $$ = 100 fb−1, MLP excludes CP mixing angles above 20° at 68% confidence level (CL), while GCN and GTN achieve exclusions at 90% CL and 95% CL, respectively. The networks also achieve a 3σ significance in excluding a pure CP-odd state.