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Jet flavour identification algorithms are of paramount importance to maximisethe physics potential of future collider experiments. This work describes anovel set of tools allowing for a realistic simulation and reconstruction ofparticle level observables that are necessary ingredients to jet flavouridentification. An algorithm for reconstructing the track parameters andcovariance matrix of charged particles for an arbitrary tracking sub-detectorgeometries has been developed. Additional modules allowing for particleidentification using time-of-flight and ionizing energy loss information havebeen implemented. A jet flavour identification algorithm based on a graphneural network architecture and exploiting all available particle levelinformation has been developed. The impact of different detector designassumptions on the flavour tagging performance is assessed using the FCC-eeIDEA detector prototype.

Jet Flavour Tagging for Future Colliders with Fast Simulation