Papers › Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
Tagging more quark jet flavours at FCC-ee at 91 GeV with a transformer-based neural network
Freya Blekman, Florencia Canelli, Alexandre De Moor, Kunal Gautam, Armin Ilg, Anna Macchiolo, Eduardo Ploerer
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Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, 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 b- and c-jet identification, supplemented by additional information that is not always included in tagging algorithms at the LHC, such as reconstructed K_S⁰ and Λ⁰ and K^±/π^± discrimination. The model is trained as a multiclassifier to identify all quark flavours separately and performs excellently in identifying b- and c-jets. An s-tagging efficiency of 40% can be achieved with a 10% ud-jet background efficiency. The performance improvement achieved by including K_S⁰ and Λ⁰ reconstruction and K^±/π^± discrimination is presented. The algorithm is applied on exclusive Z →qq̅ samples to examine the physics potential and is shown to isolate Z →ss̅ events. Assuming all non-Z →qq̅ backgrounds can be efficiently rejected, a 5σ discovery significance for Z →ss̅ can be achieved with an integrated luminosity of 60 nb⁻¹ of e⁺e⁻ collisions at √(s)=91.2 GeV, corresponding to less than a second of the FCC-ee run plan at the Z boson resonance.
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