Speaker
Dr
Marcin Wolter
(Henryk Niewodniczanski Institute of Nuclear Physics PAN)
Description
Tau leptons will play an important role in the physics program at the
LHC. They will not only be used in electroweak measurements and
in detector related studies like the determination of the E_T^miss
scale, but also in searches for new phenomena like the Higgs boson or
Supersymmetry.
Due to the overwhelming background from QCD processes, highly
efficient algorithms are essential to identify hadronically decaying
tau leptons. This can be achieved using modern multivariate techniques
which make optimal use of all the information available. They are
particularly useful in case the discriminating variables are not
independent and no single variable provides good signal and background
separation.
In ATLAS four algorithms based on multivariate techniques have been
applied to identify hadronically decaying tau leptons: projective
likelihood estimator (LL), Probability Density Estimator with Range
Searches (PDE-RS), Neural Network (NN) and Boosted Decision Trees
(BDT). All four multivariate methods applied to the ATLAS simulated
data have similar performance, which is significantly better than the
baseline cut analysis.
Primary author
Dr
Marcin Wolter
(Henryk Niewodniczanski Institute of Nuclear Physics PAN)
