Decoding the proton's gluonic density with lattice QCD-informed machine learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911073909604352 |
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| author | Kriesten, Brandon NieMiera, Alex Good, William Hobbs, T. J. Lin, Huey-Wen |
| author_facet | Kriesten, Brandon NieMiera, Alex Good, William Hobbs, T. J. Lin, Huey-Wen |
| contents | 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 $a\! \approx\! 0.09$ fm and a pion mass of $M_π\! \approx\! 310$ MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high-$x$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding the proton's gluonic density with lattice QCD-informed machine learning Kriesten, Brandon NieMiera, Alex Good, William Hobbs, T. J. Lin, Huey-Wen High Energy Physics - Phenomenology High Energy Physics - Lattice Nuclear Theory 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 $a\! \approx\! 0.09$ fm and a pion mass of $M_π\! \approx\! 310$ MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high-$x$ 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. |
| title | Decoding the proton's gluonic density with lattice QCD-informed machine learning |
| topic | High Energy Physics - Phenomenology High Energy Physics - Lattice Nuclear Theory |
| url | https://arxiv.org/abs/2507.17810 |