Using boosted decision trees for tau identification in the ATLAS experiment

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2009
Keywords: 
Multivariate techniques
Boosted decision trees
Large hadron collider
Tau leptons
Particle identification
Tau
LHC
ATLAS
Identification
Abstract: 

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 $\rightarrow \tau \tau$) and SUSY particles. Taus have a short lifetime ($c \tau= 87$ $\mu$m) 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.

Language: 
English
Document type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed, but not for the text to be copied and pasted.
Senior supervisor: 
D
Department: 
Dept. of Physics - Simon Fraser University
Thesis type: 
Thesis (M.Sc.)
Statistics: