Bayesian inference of the fluctuating proton shape in DIS and hadronic collisions

TL;DR

This paper uses Bayesian inference within the Color Glass Condensate framework to determine the fluctuating proton shape parameters, constrained by HERA and LHC data, enhancing understanding of proton structure in high-energy collisions.

Contribution

It introduces a Bayesian method to infer the fluctuating proton geometry parameters using experimental data from HERA and LHC within the CGC framework.

Findings

HERA data constrains proton shape parameters effectively.

LHC Pb+Pb collision simulations provide complementary constraints.

The approach improves understanding of proton fluctuations in high-energy physics.

Abstract

We determine the likelihood distribution for the model parameters describing the event-by-event fluctuating proton geometry at small $x$ by performing a Bayesian analysis within the Color Glass Condensate framework. The exclusive $\mathrm{J}/\psi$ production data from HERA is found to constrain the model parameters well, and we demonstrate that complementary constraints can be obtained from simulations of Pb+Pb collisions at the LHC.