Speaker
Description
Vector-like quarks (VLQs) are hypothetical particles that may lead to new physics phenomena, resolving the hierarchy problem. This talk presents a search for vector-like B quarks decaying into a top quark and a W boson, using the full CMS Run 2 proton-proton collision data at √s=13 TeV. The search targets single-lepton final states that contain one well-reconstructed muon or electron. The mass of the vector-like quark candidate is reconstructed from the lepton, hadronic jets and the missing transverse momentum. This talk will highlight the expected improvements from the optimization of event selection and object identification and the exploration of background estimation using machine learning techniques derived from neural autoregressive flows (NAF).
