TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysProc.1.018
TI  - Monte Carlo, fitting and Machine Learning for Tau leptons
PY  - 2019/02/19
UR  - https://scipost.org/SciPostPhysProc.1.018
JF  - SciPost Physics Proceedings
JA  - SciPost Phys. Proc.
VL  - 
IS  - 1
SP  - 018
A1  - Cherepanov, Vladimir
AU  - Richter-Was, Elzbieta
AU  - Was, Zbigniew Andrzej
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysProc.1.018,
	title={{Monte Carlo, fitting and Machine Learning for Tau leptons}},
	author={Vladimir Cherepanov and Elzbieta Richter-Was and Zbigniew Was},
	journal={SciPost Phys. Proc.},
	pages={018},
	year={2019},
	publisher={SciPost},
	doi={10.21468/SciPostPhysProc.1.018},
	url={https://scipost.org/10.21468/SciPostPhysProc.1.018},
}