Graph Neural Network-based Event Classification for Measurement of the Higgs-Top Yukawa Interaction

The measurement of the associated production of Higgs boson with a top-quark pair (ttH) at the LHC provides a direct determination of the Higgs-Top Yukawa interaction. The presence of a large number of objects in the final state makes the measurement very challenging. Multivariate Analysis methods such as Boosted Decision Trees (BDT) were used to enhance the analysis sensitivity. However, the sensitivity gain largely depends on physics-inspired input discriminating variables, which in practice require a significant amount of time to engineer carefully. In this presentation, we separate ttH signal from its main background using a Graph Neural Network (GNN), in which collision data are represented by graphs of nodes and edges. The graph representation and message passing of the GNN make the exploitation of relational information more effective between final state particles. We will report results using simulated events and compare GNN-based and BDT-based event classifiers. We will also examine how well the GNN model captures relational information in the event, which is challenging to represent in conventional BDT-based models.