# Quantifying Correlated Truncation Errors in Effective Field Theory

## TL;DR

This paper introduces a Bayesian Gaussian process-based model to quantify correlated truncation errors in effective field theories, improving the accuracy and validation of EFT predictions, demonstrated on nucleon-nucleon scattering data.

## Contribution

It develops a novel Gaussian process framework for correlated EFT truncation errors, enabling better uncertainty quantification and validation tools.

## Key findings

- Effective Bayesian model for correlated EFT errors
- Improved credible interval estimation for EFT predictions
- Validated approach on nucleon-nucleon scattering data

## Abstract

Effective field theories (EFTs) organize the description of complex systems into an infinite sequence of decreasing importance. Predictions are made with a finite number of terms, which induces a truncation error that is often left unquantified. We formalize the notion of EFT convergence and propose a Bayesian truncation error model for predictions that are correlated across the independent variables, e.g., energy or scattering angle. Central to our approach are Gaussian processes that encode both the naturalness and correlation structure of EFT coefficients. Our use of Gaussian processes permits efficient and accurate assessment of credible intervals, allows EFT fits to easily include correlated theory errors, and provides analytic posteriors for physical EFT-related quantities such as the expansion parameter. We demonstrate that model-checking diagnostics---applied to the case of multiple curves---are powerful tools for EFT validation. As an example, we assess a set of nucleon-nucleon scattering observables in chiral EFT. In an effort to be self contained, appendices include thorough derivations of our statistical results. Our methods are packaged in Python code, called gsum, that is available for download on GitHub.