Bayesian priors and nuisance parameters

TL;DR

The paper introduces a Bayesian method to eliminate nuisance parameters in spectral function extraction from correlators, demonstrating its effectiveness in some cases and limitations in others.

Contribution

A novel Bayesian approach to remove nuisance parameters from spectral function analysis, improving the accuracy of lattice QCD results.

Findings

Method successfully extracts pion mass with staggered quarks

Technique fails in certain cases, indicating limitations

Provides practical examples of the method's application

Abstract

Bayesian techniques are widely used to obtain spectral functions from correlators. We suggest a technique to rid the results of nuisance parameters, ie, parameters which are needed for the regularization but cannot be determined from data. We give examples where the method works, including a pion mass extraction with two flavours of staggered quarks at a lattice spacing of about 0.07 fm. We also give an example where the method does not work.