Evolutionary Fitting Methods for the Extraction of Mass Spectra in Lattice Field Theory

TL;DR

This paper introduces an evolutionary algorithm approach for extracting particle mass spectra from lattice QCD correlators, dynamically adjusting the number of states to optimize fit quality.

Contribution

It presents a novel application of evolutionary algorithms to lattice QCD data analysis, enabling adaptive fitting of multiple states for improved mass spectrum extraction.

Findings

Effective dynamic adjustment of the number of states in fits.

Improved accuracy in mass spectrum extraction.

Potential for broader application in lattice field theory analysis.

Abstract

We present an application of evolutionary algorithms to the curve-fitting problems commonly encountered when trying to extract particle masses from correlators in Lattice QCD. Harnessing the flexibility of evolutionary methods in global optimization allows us to dynamically adapt the number of states to be fitted along with their energies so as to minimize overall \chi^2/(d.o.f.), leading to a promising new way of extracting the mass spectrum from measured correlation functions.