Closure Testing the MSHT PDFs and a First Direct Comparison to the Neural Net Approach

TL;DR

This paper conducts a closure test of the MSHT20 PDF fitting approach, compares it directly to NNPDF methodology, and finds MSHT's parameterisation is flexible and yields slightly better fits with comparable uncertainties.

Contribution

It provides the first closure test of the MSHT20 PDFs and a direct, like-for-like comparison between MSHT and NNPDF fitting methods.

Findings

MSHT20 parameterisation reproduces input features within uncertainties.

MSHT fits are slightly better than NNPDF4.0 in quality.

Uncertainty estimates depend on the tolerance used in the fit.

Abstract

We present a brief overview of the first global closure test of the fixed parameterisation (MSHT) approach to PDF fitting. We find that the default MSHT20 parameterisation can reproduce the features of the input set in such a closure test to well within the textbook uncertainties. This provides strong evidence that parameterisation inflexibility in the MSHT20 fit is not a significant issue in the data region. We also present the first completely like-for-like comparison between two global PDF fits, namely MSHT and NNPDF, where the only difference is guaranteed to be due to the fitting methodology. To achieve this, we present a fit to the NNPDF4.0 data and theory inputs with the MSHT parameterisation. We find that this gives a moderately, but noticeably, better fit quality than the central NNPDF4.0 fits and that this difference persists at the level of the PDFs and benchmark cross sections. The NNPDF4.0 uncertainties are found to be broadly in line with the MSHT results if a textbook $T^2=1$ tolerance is applied, but to be significantly smaller if a tolerance typical of the MSHT20 fit is applied.