Speaker
Description
New physics searches in the highly boosted regime are an essential part of LHC's physics program, aiming at revealing the presence of new heavy resonances predicted by many Beyond Standard Model theories on the high-end of LHC's energy reach.
Within the CMS collaboration numerous jet tagging algorithms have been developed for the identification of hadronic jets originating from the decay of standard model particles such as top quark, Higgs bosons or vector bosons carrying a large Lorentz boost in the CMS rest frame. In these cases, the structure from the heavy particle decay chain reflects onto the distributions and features of the constituents of the jet itself.
Modern Machine Learning algorithms are perfectly suited for the task of exploiting such features to identify these jets, discriminating both among different heavy particle hypotheses and against the jets originating from Quantum Chromo Dynamics background processes. These tagging techniques are as of today very widely and successfully used in physics analyses to improve signal reconstruction and background rejection.
This contribution showcases the state-of-the-art Machine Learning techniques that have been adopted in CMS for heavy object jet tagging in new physics searches, describing their performances in analyses deployed on data collected by CMS during the so-called Run-II and Run-III data taking periods at center-of-mass energies of 13 and 13.6 TeV, respectively.
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