Ground state mass in short lattices by controlling overconfidence and bias in Bayesian fits

TL;DR

This paper introduces a Bayesian method to accurately extract ground-state masses from short lattice simulations by managing overconfidence and bias, enabling reliable results despite limited lattice length.

Contribution

It presents a novel black-box Bayesian approach that controls meta-parameters to overcome biases in short lattice data for ground-state mass extraction.

Findings

Effective extraction of ground-state mass from short lattices.

Controlling overconfidence improves data-driven results.

Method applicable as a black-box tool for lattice simulations.

Abstract

We investigate the seemingly ill-defined problem of extracting a ground-state mass from a lattice simulation where the extent of the lattice is not long enough to project out the ground-state properly. We regulate the problem using a Bayesian method. We show that controlling meta-parameters (overconfidence) can allow the data to overcome the input priors (bias). We can write the method as a black-box technique which allows extraction of a ground-state mass, even on a relatively short lattice.