Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates

2004/01/09 by Joan Rojo, Jose I. Latorre, Jose I Latorre · 3 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Function (biology) #Hadron #Parametrization (atmospheric modeling) #Particle physics theoretical and experimental studies #Pion #QCD sum rules #Quantum Chromodynamics and Particle Interactions #Quantum chromodynamics #Spectral function #Sum rule in quantum mechanics #hep-ph

paper · pdf · doi:10.1088/1126-6708/2004/01/055

published as JHEP 0401:055,2004 · 29 pages, 18 ps figures

arxiv created 2004/01/09 · openalex publication_date 2004/01/27 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05

Abstract

The spectral function ρ_V-A(s) is determined from ALEPH and OPAL data on hadronic tau decays using a neural network parametrization trained to retain the full experimental information on errors, their correlations and chiral sum rules: the DMO sum rule, the first and second Weinberg sum rules and the electromagnetic mass splitting of the pion sum rule. Nonperturbative QCD vacuum condensates can then be determined from finite energy sum rules. Our method minimizes all sources of theoretical uncertainty and bias producing an estimate of the condensates which is independent of the specific finite energy sum rule used. The results for the central values of the condensates O₆ and O₈ are both negative.

Citations

• Constraints on chiral perturbation theory parameters from QCD inequalities
• Chiral perturbation theory to one loop
• Decay Correlations of Heavy Leptons ine++e−→l++l−
• QCD condensates for the light quarkV−Acorrelator
• Matching the electroweak penguins Q7,Q8 and spectral correlators

Cited by

• Geometric approach to condensates in holographic QCD
• Neural network-based top tagger with two-point energy correlations and geometry of soft emissions
• A first unbiased global NLO determination of parton distributions and their uncertainties