A graph neural network for deep learning-based full event interpretation at FCC-ee

Abstract

This paper presents a deep learning-based full event interpretation prototype developed for the electron-positron Future Circular Collider (FCC-$ee$) using simulated $e^{+}e^{-}\rightarrow Z\rightarrow b\bar{b}$ events at $\sqrt{s} = 91 \ \mathrm{GeV}$. The study was carried out using the Innovative Detector for Electron positron Accelerators (IDEA) detector concept as case study for future application. The proposed graph neural network framework aims to simultaneously identify and reconstruct the hierarchical decay trees of $b$-hadrons in each event using information from reconstructed charged particles. Events are represented as graphs, where reconstructed charged particles form the nodes and pairwise observables are encoded as edge features describing the relations between particles. On a test sample, 90% of reconstructed decay chains contain exactly the correct set of particles, irrespective of their inferred decay hierarchy. Across a range of benchmark $b$-hadron decays, this clustering efficiency exceeds 93% for decay chains spanning up to three generations of decays.