Speaker
Prof.
Dugan O'Neil
(Simon Fraser University (SFU))
Description
Tau leptons will play an important role in the physics program at the
LHC. They will be used in electroweak measurements and in detector
related studies like the determination of the missing transverse
energy scale, but also in searches for new phenomena like the Higgs
boson or Supersymmetry.
Due to the huge background from QCD processes, efficient tau
identification techniques with large fake rejection are essential. Tau
object appear as collimated jets with low track multiplicity and
single variable criteria are not enough to efficiently separate them
from jets and electrons. This can be achieved using modern
multivariate techniques which make optimal use of all the information
available. They are particularly useful when the discriminating
variables are not independent and no single variable provides good
signal and background separation.
In ATLAS several advanced algorithms are applied to identify taus, in
particular a projective likelihood estimator and boosted decision trees.
All multivariate methods applied to the ATLAS simulated data perform
better than the baseline cut analysis. Their performance is shown
using high energy data collected at the ATLAS experiment. The
strengths and weaknesses of each technique are also discussed.
Author
Prof.
Dugan O'Neil
(Simon Fraser University (SFU))
