Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network.

Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJet- Transformer, is presented, which exploits a transformer-based neural network that is substantially faster to train than state-of-the-art graph neural networks. The DeepJetTransformer algorithm uses information from particle flow-style objects and secondary vertex reconstruction for - and -jet identification, supplemented by additional information that is not always included in tagging algorithms at the LHC, such as reconstructed and and discrimination. The model is trained as a multiclassifier to identify all quark flavours separately and performs excellently in identifying - and -jets. An -tagging efficiency of can be achieved with a -jet background efficiency. The performance improvement achieved by including and reconstruction and discrimination is presented. The algorithm is applied on exclusive samples to examine the physics potential and is shown to isolate events. Assuming all non- backgrounds can be efficiently rejected, a discovery significance for can be achieved with an integrated luminosity of of collisions at , corresponding to less than a second of the FCC-ee run plan at the boson resonance.