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Monte Carlo, fitting and Machine Learning for Tau leptons

by V. Cherepanov, E. Richter-Was, Z. Was

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Submission summary

Authors (as Contributors): Zbigniew Andrzej Was
Submission information
Arxiv Link: (pdf)
Date accepted: 2019-01-15
Date submitted: 2018-12-11 01:00
Submitted by: Was, Zbigniew Andrzej
Submitted to: SciPost Physics Proceedings
Proceedings issue: The 15th International Workshop on Tau Lepton Physics (Amsterdam, 2018-09)
Ontological classification
Academic field: Physics
  • High-Energy Physics - Phenomenology
Approaches: Theoretical, Computational


Status of tau lepton decay Monte Carlo generator TAUOLA, and its main recent applications are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the experimental data must be taken with a great care. Studies for H to tau tau; tau to hadrons indeed demonstrate that multi-dimensional nature of distributions is important and available for evaluation of observables where tau leptons are used to constrain experimental data. For that part of the presentation, use of the TAUOLA program for phenomenology of H and Z decays at LHC is discussed, in particular in the context of the Higgs boson parity measurements with the use of Machine Learning techniques. Some additions, relevant for QED lepton pair emission and electroweak corrections are mentioned as well.

Published as SciPost Phys. Proc. 1, 018 (2019)

Submission & Refereeing History

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Resubmission 1811.03969v3 on 11 December 2018
Submission 1811.03969v1 on 12 November 2018

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