Speaker
Description
Jet flavour identification algorithms are essential to maximise the physics output in e+e- collisions at the Future Circular Collider (FCC-ee). Within the broad FCC-ee physics programme, jet flavour tagging plays a central role in QCD studies as well as in precision measurements of the Higgs boson, electroweak bosons, and top quark, owing to the dominance of hadronic decays of the heaviest Standard Model (SM) particles. Highly efficient discrimination among bottom-, charm-, strange-, and gluon-initiated jets enables precisely measuring decay channels that are inaccessible at the LHC, thereby significantly enhancing the sensitivity of precision SM measurements and searches for new physics at FCC-ee. In this contribution, we present recent developments in jet flavour tagging based on machine learning techniques applied to representative FCC-ee physics studies. In addition to excellent performance in b- and c-jet identification, future taggers are expected to achieve high discrimination for strange-quark jets, opening the possibility of measuring the strange-quark Yukawa coupling as well as probing rare flavour-changing neutral current processes such as $Z \to bs$. Potential strategies for calibrating jet tagging performance will be also discussed.