Monte Carlo, fitting and Machine Learning for Tau leptons

Abstract

Status of τ 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 → τ τ ; τ → hadrons indeed demonstrate that multi-dimensional nature of distributions is important and available for evaluation of observables where τ 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.

Analysis of the CP structure of the Yukawa coupling between the Higgs boson and τ leptons in proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV

“…Therefore, we only apply the polarimetric vector method to the a 3pr 1 a 3pr 1 decay configuration. The polarimetric vector h can be considered as an estimate of the most likely direction of the spin vector s of the τ lepton in the τ lepton rest frame [88]. We start by outlining the reconstruction of the τ lepton momenta, which are required to compute the polarimetric vectors.…”

“…Various techniques can be used to define φ CP depending on the decay topology of the τ leptons. In total, four methods are employed in the analysis: the "impact parameter" [85,86], "neutral-pion" [86,87], "combined" [86], and "polarimetric vector" [88] methods. We provide a detailed description of these methods below.…”