[Submitted on 14 Apr 2026] (cross-list from hep-ph)
Proton Structure from Neural Simulation-Based Inference at the LHC
Ricardo Barrué🇦🇹 · Lisa Benato🇦🇹 · Ali Kaan Güven🇦🇹 · Elie Hammou🇳🇱 · Jaco ter Hoeve🇬🇧 · Claudius Krause🇦🇹 · Ang Li🇦🇹 · Luca Mantani🇪🇸 · Juan Rojo🇳🇱 · Sergio Sánchez Cruz🇪🇸 · Robert Schöfbeck🇦🇹 · Maria Ubiali🇬🇧 · Daohan Wang🇦🇹
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 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.
- Comments:
- 57 pages, 24 figures
- Subjects:
- High Energy Physics — Phenomenology (hep-ph); High Energy Physics — Experiment (hep-ex); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th)
- arXiv:
- 2604.13157 [pdf]