[Submitted on 23 Jul 2025] (cross-list from hep-ph)
Decoding the proton's gluonic density with lattice QCD-informed machine learning
Brandon Kriesten🇺🇸 · Alex NieMiera🇺🇸 · William Good🇺🇸 · T.J. Hobbs🇺🇸 · Huey-Wen Lin🇺🇸
We present a first machine learning-based decoding of the gluonic structure of the proton from lattice QCD using a variational autoencoder inverse mapper (VAIM). Harnessing the power of generative AI, we predict the parton distribution function (PDF) of the gluon given information on the reduced pseudo-Ioffe-time distributions (RpITDs) as calculated from an ensemble with lattice spacing fm and a pion mass of MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high- region where lattice data are most constraining. A subsequent correlation analysis confirms that the VAIM learns a meaningful latent representation, highlighting the potential of generative AI to bridge lattice QCD and phenomenological extractions within a unified analysis framework.
- Comments:
- 7 pages, 3 figures
- Subjects:
- High Energy Physics — Phenomenology (hep-ph); High Energy Physics — Lattice (hep-lat); Nuclear Theory (nucl-th)
- arXiv:
- 2507.17810 [pdf]