Proton Structure from Neural Simulation-Based Inference at the LHC

TL;DR

This paper demonstrates the first use of neural simulation-based inference to determine proton PDFs from high-dimensional unbinned LHC data, improving precision over traditional binned methods.

Contribution

It introduces a novel unbinned inference approach for proton PDF determination using neural simulation-based inference at the LHC.

Findings

NSBI effectively constrains the gluon PDF from simulated top quark data.

Unbinned data analysis enhances precision compared to binned analyses.

The method accounts for systematic uncertainties, reducing reliance on approximations.

Abstract

The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity LHC. So far, PDFs are determined from global fits to binned low-dimensional data obtained from unfolded hard-scattering cross section measurements. In this work we demonstrate for the first time the feasibility of neural simulation-based inference (NSBI) for constraining the proton PDFs using a high-dimensional unbinned data set. Exploiting the full statistical power of unbinned data removes the loss of information inherited by the binning procedure. As a proof-of-concept, we determine the gluon PDF from simulated data of top quark pair production at the LHC with $\sqrt{s}=13$ TeV. Taking into account both experimental and theoretical systematic uncertainties in the detector-level features, we demonstrate how the NSBI pipeline achieves significant improvements in precision compared to existing low-dimensional binned analyses. Our results illustrate the potential of unbinned inference to reduce the reliance on coarse approximations of uncertainties and their correlations entering PDF determinations, hence contributing to a new paradigm of unbinned detector-level ML-assisted measurements at the LHC.