Speaker
Koen Denekamp
(IMAPP)
Description
Accurate machine learning models are a requirement in modern experimental particle physics. For many analyses, it is important to be able to accurately separate certain signals and backgrounds from each other. In this study, I perform an analysis using Boosted Decision Trees, Neural Networks, and a combination on simulated LHCb data and compare their effectiveness. This work is done in the context of Dr. S. Klaver’s research into lepton flavour universality in $B^0_s$ decays.
Author
Koen Denekamp
(IMAPP)
