Speaker
Description
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.
The DeepJetTransformer network uses information from particle flow-style objects and secondary vertex reconstruction, as is standard for $b$- and $c$-jet identification, supplemented by additional information, such as reconstructed V$^0$s and $K^{\pm}/\pi^{\pm}$ discrimination, typically not included in tagging algorithms at the LHC. 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 impact of including V$^0$s and $K^{\pm}/\pi^{\pm}$ discrimination is presented.
The network is applied on exclusive $Z\to q\bar{q}$ samples to examine the physics potential and is shown to isolate $Z\to s\bar{s}$ events. Assuming all other backgrounds can be efficiently rejected, a $5\sigma$ discovery significance for $Z\to s\bar{s}$ can be achieved with an integrated luminosity of $60~\text{nb}^{-1}$, corresponding to less than a second of the FCC-ee run plan at the $Z$ resonance.
To appreciate how powerful these modern tools are from a physics perspective, the potential for a precision measurement of the forward-backward asymmetry at the Z pole will be discussed.
