Jet Flavour Tagging for Future Colliders with Fast Simulation

TL;DR

This paper introduces a comprehensive simulation and reconstruction framework for jet flavour tagging at future colliders, utilizing advanced algorithms and neural networks to improve particle identification and assess detector design impacts.

Contribution

It presents new tools for realistic particle-level simulation, a novel track reconstruction algorithm, and a graph neural network-based jet flavour tagging method tailored for future collider experiments.

Findings

Enhanced jet flavour tagging performance demonstrated.

Impact of detector design on tagging efficiency analyzed.

Integration of particle identification techniques improves accuracy.

Abstract

Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype.