/CERN-Machine-Learning-Research

work on vector boson scattering signal and background events classification at the European Organization for Nuclear Research (CERN)'s ATLAS experiment of the Large Hadron Collider (LHC)

Primary LanguagePython

The European Organization for Nuclear Research (CERN) Machine Learning Research

A short summary of this research could be found at Research_Summary.pdf and Research Poster.pdf

▪ Implemented a set of analysis scripts in Python and Bash under ROOT framework, including preprocessing events data and building the training/testing pipelines of different Decision Tree models (Boosted Gradient, Adaptive, with Fisher discriminant and decorrelation).
▪ Improved signal efficiency by 7% and background rejection by 12.5%; replaced the old events classification model, which was based on threshold cuts of discriminant variables.