Speaker
Description
Rare vector boson scattering (VBS) processes are of particular interest for testing the Standard Model and searching for possible new physics. However, their experimental study is challenging because of the need to isolate a weak signal against the background of much more probable processes, such as the production of vector boson pairs via diagramms with presence of strong interactions (QCD), as well as other processes with similar final states and significantly larger cross sections.
This work investigates the possibility of improving the efficiency of separating VBS events from background processes using additional variables related to the characteristics of the third jet, as well as modern machine learning algorithms. Particular attention is paid to the careful consideration of statistical and systematic uncertainties, which is critically important when analyzing such rare processes.
The study demonstrates the potential for improving sensitivity to the VBS signal by optimizing the selection of features and applying machine learning methods.
