Speaker
Description
Measurement of electroweak (EWK) component of associated $Z\gamma$ production is a precise test of the electroweak theory and a probe of BSM theories that predict anomalous vector boson self-couplings. However, recognition of it in the presence of QCD $Z\gamma$ production component on hadron collider experiment is a very challenging task due to identical final states and higher cross section of the background process. The main difference is the origin of two leading jets in these two processes. Machine learning (ML) algorithms allow one to use jet kinematic variables and kinematic balances of the whole system more effectively increasing the significance of the measurement. A new algorithm for the construction and selection of the input kinematic observables for the ML classifier was used with the ultimate goal of better separation EWK and QCD $Z\gamma$ production components. The expected significance of distinguished EWK production measurement for LHC experiments conditions at the second datataking period (Run2) with 140 fb$^{−1}$ amount of data was estimated.
