Bayesian constraint of the initial condition for the Balitsky-Kovchegov equation at NLO

TL;DR

This paper employs Bayesian inference to tightly constrain the initial conditions of the NLO Balitsky-Kovchegov equation using HERA data, enabling better uncertainty quantification in CGC calculations.

Contribution

It introduces a Bayesian framework to determine initial conditions for the NLO BK equation, integrating experimental data to quantify uncertainties.

Findings

Stringent constraints on initial amplitude parameters

Successful NLO description of HERA data

Quantified uncertainties for CGC calculations

Abstract

We use Bayesian inference to constrain the parameters describing the initial amplitude input to the Balitsky-Kovchegov evolution equation at next-to-leading order accuracy against precise HERA total inclusive cross section and heavy quark data. The datasets are found to provide stringent constraints and, with consistent NLO treatment, a successful description of the data is obtained. The posterior distributions define the theoretical uncertainites that surround the non-perturbative initial condition and, thus, provide a way to propagate said uncertainties to CGC calculations at NLO.