Pith Number

pith:26YZJHXE

pith:2026:26YZJHXEXX24EFRKU5ECKBCIVX

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Neural Network Generalized Parton Distributions (NNGPD)

Simonetta Liuti, Zaki Panjsheeri

Neural networks can reconstruct generalized parton distributions by training on both experimental data and lattice QCD results.

arxiv:2605.13000 v1 · 2026-05-13 · hep-ph

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD).

C2weakest assumption

That a neural network trained on available data and LQCD results can accurately and unbiasedly reconstruct the full GPD functions without overfitting or missing important physical constraints.

C3one line summary

A deep learning-assisted framework extracts generalized parton distributions from experimental data and ab-initio lattice QCD results.

References

15 extracted · 15 resolved · 0 Pith anchors

[1] AI for nuclear physics: the EXCLAIM project.JINST, 20(08):C08011, 2025 2025

[2] Gauge-InvariantDecompositionofNucleonSpin.Phys 1997

[3] A. V. Radyushkin. Nonforward parton distributions.Phys. Rev. D, 56:5524–5557, 1997 1997

[4] R. L. Jaffe and Aneesh Manohar. The𝑔1 Problem: Fact and Fantasy on the Spin of the Proton.Nucl. Phys. B, 337:509–546, 1990 1990

[5] Generalized Parton Distributions from Symbolic Regression 2025

Receipt and verification

First computed	2026-05-18T03:09:00.435772Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d7b1949ee4bdf5c2162aa748250448adf30f69819d4a0856fa35fddea82c9bfd

Aliases

arxiv: 2605.13000 · arxiv_version: 2605.13000v1 · doi: 10.48550/arxiv.2605.13000 · pith_short_12: 26YZJHXEXX24 · pith_short_16: 26YZJHXEXX24EFRK · pith_short_8: 26YZJHXE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/26YZJHXEXX24EFRKU5ECKBCIVX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d7b1949ee4bdf5c2162aa748250448adf30f69819d4a0856fa35fddea82c9bfd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "75436de947cc073b9ecb514bc2a81d4f60c456bb677988b6dafea7246152bab8",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "hep-ph",
    "submitted_at": "2026-05-13T04:56:42Z",
    "title_canon_sha256": "6efa7df410e8dcfc0ad713e1840865456013468d43c9b679e997605c26a4fe68"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13000",
    "kind": "arxiv",
    "version": 1
  }
}