Pith Number

pith:V26P7UMM

pith:2026:V26P7UMMW6MH6HLJMGEYR63W6K

not attested not anchored not stored refs pending

TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging

Alexei Prokudin, Daniel Pitonyak, Jian-Wei Qiu, Leonard Gamberg, Marco Zaccheddu, Nobuo Sato, Wally Melnitchouk

A nonparametric pixel-based framework with generative AI solves the TMD inverse problem for unbiased parton imaging.

arxiv:2605.06606 v2 · 2026-05-07 · hep-ph

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\usepackage{pith}
\pithnumber{V26P7UMMW6MH6HLJMGEYR63W6K}

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Record completeness

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4 Citations open

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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

The new framework provides the first integration of pixel-based discretization, generative AI, and SVD within a Bayesian context to solve the TMD inverse problem. This synergy between machine learning and multi-scale data removes inherent degeneracies and enables unbiased 3D partonic imaging.

C2weakest assumption

That the hybrid normalizing flow-driven Metropolis-Hastings sampler achieves efficient and exact sampling of the high-dimensional posterior without introducing biases that affect the reconstructed TMDs or the identification of null components.

C3one line summary

A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by observables.

Receipt and verification

First computed	2026-05-20T02:05:44.463637Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

aebcffd18cb7987f1d69618988fb76f2a258d37e142f858717e4a988409b9162

Aliases

arxiv: 2605.06606 · arxiv_version: 2605.06606v2 · doi: 10.48550/arxiv.2605.06606 · pith_short_12: V26P7UMMW6MH · pith_short_16: V26P7UMMW6MH6HLJ · pith_short_8: V26P7UMM

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/V26P7UMMW6MH6HLJMGEYR63W6K \
  | 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: aebcffd18cb7987f1d69618988fb76f2a258d37e142f858717e4a988409b9162

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7eaa5fdad6959d41d33f9a41c484721a804c0484224020921aa385b389231219",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "hep-ph",
    "submitted_at": "2026-05-07T17:25:41Z",
    "title_canon_sha256": "fd1ac899adcdf98dc68a0d0b6b65abd85eb18402735d5f42b7328731b7d64c08"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.06606",
    "kind": "arxiv",
    "version": 2
  }
}