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Monte Carlo, fitting and Machine Learning for Tau leptons
by V. Cherepanov, E. Richter-Was, Z. Was
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Zbigniew Andrzej Was |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/1811.03969v3 (pdf) |
| Date accepted: | Jan. 15, 2019 |
| Date submitted: | Dec. 11, 2018, 1 a.m. |
| Submitted by: | Was, Zbigniew Andrzej |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 15th International Workshop on Tau Lepton Physics (TAU2018) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
Abstract
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)