• Łukasz Janyst Jagiellonian University, Krakow
  • Anna Kaczmarska Polish Academy of Sciences
  • Tadeusz Szymocha Polish Academy of Sciences
  • Marcin Wolter Polish Academy of Sciences
  • Andrzej Zemła Polish Academy of Sciences




multivariate methods, High Energy Physics, ATLAS, tau leptons


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.


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Author Biographies

Łukasz Janyst, Jagiellonian University, Krakow

Faculty of Physics, Astronomy and Applied Computer Science

Anna Kaczmarska, Polish Academy of Sciences

Institute of Nuclear Physics

Tadeusz Szymocha, Polish Academy of Sciences

Institute of Nuclear Physics

Marcin Wolter, Polish Academy of Sciences

Institute of Nuclear Physics

Andrzej Zemła, Polish Academy of Sciences

Institute of Nuclear Physics


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How to Cite

Janyst, Łukasz, Kaczmarska, A., Szymocha, T., Wolter, M., & Zemła, A. (2013). OPTIMIZATION OF TAU IDENTIFICATION IN ATLAS EXPERIMENT USING MULTIVARIATE TOOLS. Computer Science, 9(3), 35. https://doi.org/10.7494/csci.2008.9.3.35




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