Monte Carlo, fitting and Machine Learning for Tau leptons

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.

11 pages 2 figures 2 tables. Corrections required by the conference introduced, incomplete equation (3) required fix for that equation (4) introduced

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• A New Mass Reconstruction Technique for Resonances Decaying to di-tau
• Potential for optimizing Higgs boson CP measurement in H to tau tau decay at LHC and ML techniques
• Deep learning approach to the Higgs boson CP measurement in H to tau tau decay and associated systematics
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Cited by in corpus (2)

• Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC
• LHC Higgs CP Sensitive Observables in H to tau^+ tau^-; tau^pm to (3pi)^pm nu and Machine Learning Benefits