Speaker
Tapio Lampen
(Helsinki Institute of Physics HIP)
Description
We demonstrate the use of a ROOT Toolkit for Multivariate Data
Analysis (TMVA) in tagging b-jets associated with heavy
neutral MSSM Higgs bosons at the LHC.
The associated b-jets can be used to extract Higgs events from the
Drell-Yan background, for which the associated jets are mainly light
quark and gluon jets.
TMVA provides an evaluation for different multivariate
classification techniques wrapped in a ROOT-integrated
machine learning environment. Background
discriminating power is demonstrated for various methods available in
TMVA, such as rectangular cut optimisation, projective and
multi-dimensional likelihood estimators, linear discriminant
analysis with H-Matrix and Fisher discriminants, artificial
neural networks and boosted/bagged decision trees.
The effect of choice of variables and variable transformation is described. TMVA
working in transparent factory mode guarantees an unbiased
performance comparison, since all classifiers are evaluated
with the same training and test data. Finally, results are
compared against previous studies using neural networks
and standard methodology where associated b-jets
can be identified using lifetime based tagging algorithms,
which rely on displaced secondary vertices and track impact
parameters.
Submitted on behalf of Collaboration (ex, BaBar, ATLAS) | CMS |
---|
Primary authors
Aatos Heikkinen
(Helsinki Institute of Physics HIP)
Tapio Lampen
(Helsinki Institute of Physics HIP)
Tomas Linden
(Helsinki Institute of Physics HIP)
Co-authors
Francisco Garcia
(Helsinki Institute of Physics HIP)
Lauri Wendland
(Helsinki Institute of Physics HIP)
Matti Kortelainen
(Helsinki Institute of Physics HIP)
Pekka Kaitaniemi
(Helsinki Institute of Physics HIP)
Sami Lehti
(Helsinki Institute of Physics HIP)
Veikko Karimaki
(Helsinki Institute of Physics HIP)