# Towards tests of quark-hadron duality with functional analysis and spectral function data

## TL;DR

This paper develops and tests a functional analysis method to detect quark-hadron duality violations in spectral function data, demonstrating its effectiveness with simulated datasets inspired by experimental measurements.

## Contribution

It introduces a new analytic expression for the minimal functional distance and applies it to spectral data, enabling statistically significant detection of duality violations.

## Key findings

- Functional analysis tests can detect duality violations in spectral data.
- Excluding high-error regions improves detection sensitivity.
- The method is validated with realistic toy model pseudodata.

## Abstract

The presence of terms that violate quark-hadron duality in the expansion of QCD Green's functions is a generally accepted fact. Recently, a new approach was proposed for the study of duality violations (DVs), which exploits the existence of a rigorous lower bound on the functional distance, measured in a certain norm, between a "true" correlator and its approximant calculated theoretically along a contour in the complex energy plane. In the present paper we pursue the investigation of functional-analysis based tests towards their application to real spectral function data. We derive a closed analytic expression for the minimal functional distance based on the general weighted $L^2$ norm and discuss its relation with the distance measured in $L^\infty$ norm. Using fake data sets obtained from a realistic toy model in which we allow for covariances inspired from the publicly available ALEPH spectral functions, we obtain by Monte Carlo simulations the statistical distribution of the strength parameter that measures the magnitude of the DV term added to the usual operator product expansion (OPE). The results show that, if the region with large errors near the end-point of the spectrum in $\tau$ decays is excluded, the functional-analysis based tests using either $L^2$ or $L^\infty$ norms are able to detect, in a statistically significant way, the presence of DVs in realistic spectral function pseudodata.