Speaker
Description
Jets are collimated sprays of particles resulting from the hadronization of high-energy quarks and gluons. When heavy bosons (W, Z, or Higgs) decay hadronically, the resulting quarks can be reconstructed as separate thin-radius jets. At the same time, if the bosons are produced with high transverse momentum, they can be reconstructed as a single highly collimated large-radius jet. In both scenarios, to ensure accurate measurements of particle properties, CMS applies jet energy scale corrections, which account for detector effects and calibration biases. Additionally, regression techniques, based on machine learning, are used to improve the resolution of the jet energy measurement, by incorporating additional jet substructure information. Once jet energies are corrected, CMS uses several jet flavor tagging algorithms, exploiting sophisticated machine learning techniques, to identify the particle from which the jet originates and to distinguish hadronically decaying resonances from the large QCD multijet background. The accurate determination of jet properties plays a fundamental role in Standard Model measurements, as well as in searches for new physics phenomena. This talk presents an overview of recent developments in terms of jet energy scale and resolution, substructure techniques, and jet flavour tagging algorithms.
