Muon indentification with TMVA

As I said in the meeting this morning, I am learning how to use the TMVA (Toolkit for Multivariate Data Analysis with ROOT) package to improve the signal-background separation.

My learning process started using the single-particle samples that Hajrah used to obtain selection cuts for the muon identification. I used the same distributions as the input discriminating variables into four different methods: CutsGA (rectangular cuts), Likelihood, MLP (neural networks) and BDT (boosted decision trees).

All methods can give about 99.6% efficiency at the maximum S/√(S+B), i.e. at the optimal background rejection. Previous cuts give 97.5% efficiency with similar purity.

Some nice output plots from the package can be seen in my wiki page .