Speaker
Description
A fundamental aspect of CMS researches concerns the identification and characterisation of jets originating from quarks and gluons produced in high-energy proton-proton collisions. Electroweak scale resonances (Z/W bosons), Higgs bosons and top quarks are often produced with high Lorentz-boosts, where their products become highly collimated large and massive jets, usually reconstructed as AK8 jets. Therefore, the identification of the particle initiating the jet plays a crucial role in distinguishing between boosted top quarks and bosons from the QCD background. In this talk, an overview of the usage of boosted jet taggers within CMS will be given. It will highlight the most recent AK8 tagging algorithms, which make use of sophisticated machine learning techniques, optimised for performance and efficiency. Furthermore, the presentation will show the validation of ML-based taggers, developed for AK8 jets originating from boosted resonances decaying to b$\Bar{\text{b}}$, comparing CMS data and MC simulation.
