Optimization of tau identification in ATLAS using multivariate tools

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_Tmiss 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.