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Using boosted decision trees for tau identification in the ATLAS experiment

Posted on:2010-12-14Degree:M.ScType:Thesis
University:Simon Fraser University (Canada)Candidate:Godfrey, JenniferFull Text:PDF
GTID:2444390002472646Subject:Physics
Abstract/Summary:
The ATLAS detector will begin taking data from p-p collisions in 2009. This experiment will allow for many different physics measurements and searches. The production of tau leptons at the LHC is a key signature of the decay of both the standard model Higgs (via H → tautau ) and SUSY particles. Taus have a short lifetime (ctau = 87 mum) and decay hadronically 65% of the time. Many QCD interactions produce similar hadronic showers and have cross-sections about 1 billion times larger than tau production. Multivariate techniques are therefore often used to distinguish taus from this background. Boosted Decision Trees (BDTs) are a machine-learning technique for developing cut-based discriminants which can significantly aid in extracting small signal samples from overwhelming backgrounds. In this study, BDTs are used for tau identification for the ATLAS experiment. They are a fast, flexible alternative to existing discriminants with comparable or better performance.
Keywords/Search Tags:ATLAS, Tau
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