  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.
