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Optimization Studies for the H $\rightarrow$ WW Boosted Decision Tree Analysis
The aim of this project was to follow the ATLAS $H \rightarrow WW$ BDT analysis and try to optimize training variables, pre-selection cuts, and training parameters such as the depth \cite{orig, spin}. Machine learning was done with Monte Carlo samples of the $H \rightarrow W^+ W^- \rightarrow e \mu...
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| Lenguaje: | eng |
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2014
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| Acceso en línea: | http://cds.cern.ch/record/1752226 |