Optimization Of Tau Identification In Atlas Experiment Using Multivariate Tools

Abstract

Elementary particle physics experiments, which search for very rare processes, require theefficient analysis and selection algorithms able to separate a signal from the overwhelmingbackground. Four learning machine algorithms have been applied to identify τ leptons inthe ATLAS experiment: projective likelihood estimator (LL), Probability Density Estimatorwith Range Searches (PDE-RS), Neural Network, and the Support Vector Machine (SVM).All four methods have similar performance, which is significantly better than the baselinecut analysis. This indicates that the achieved background rejection is close to the maximal achievable performance.

Keywords

• multivariate methods; High Energy Physics; ATLAS; tau leptons