Pith Number

pith:NGONV6TW

pith:2026:NGONV6TWSYK56IYY4N4U2VKVCS

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Accelerating 3D Non-LTE Synthesis with Graph Neural Networks

A. Asensio Ramos, A. Vicente Ar\'evalo, C. J. D\'iaz Baso

Graph neural networks predict 3D non-LTE calcium populations in the solar atmosphere with correlations above 0.99 and million-fold speedups.

arxiv:2605.09543 v1 · 2026-05-10 · astro-ph.SR · astro-ph.IM

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Record completeness

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Claims

C1strongest claim

The trained GNN accurately predicts populations of the five-level Ca II atom plus continuum. Correlations exceed 0.99 in the photosphere and mid-chromosphere; errors in the upper chromosphere remain unbiased. Inference is ∼10^6 times faster than traditional iterative solvers. Spectral synthesis of the Ca II 8542 Å line yields intensity profiles with < 2 % mean residuals relative to the full 3D solution.

C2weakest assumption

That a GNN trained on populations from a single Bifrost simulation snapshot will generalize accurately to other atmospheric conditions, and that the directed graph discretization with distance-based edges fully captures the essential radiative couplings without missing key non-local effects.

C3one line summary

Graph neural networks can approximate full 3D non-LTE Ca II populations in solar models with correlations above 0.99 and extreme computational efficiency.

References

16 extracted · 16 resolved · 2 Pith anchors

[1] 2017, A&A, 599, A133 Asensio Ramos, A., Trujillo Bueno, J., & Landi Degl’Innocenti, E 2017

[2] Relational inductive biases, deep learning, and graph networks 2018 · arXiv:1806.01261

[3] Carlsson, M., Hansteen, V . H., Gudiksen, B. V ., Leenaarts, J., & De Pontieu, B. 2015, Astronomy & Astrophysics, 585, A4 2015

[4] Chappell, B. A. & Pereira, T. M. D. 2022, A&A, 658, A182 2022

[5] Cheung, M. C. M., Rempel, M., Chintzoglou, G., et al. 2019, Nature Astronomy, 3, 160 de la Cruz Rodríguez, J., Leenaarts, J., Danilovic, S., & Uitenbroek, H. 2019, A&A, 623, A74 Díaz Baso, C. J., Asen 2019

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-19T16:10:38.723618Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

699cdafa769615df2318e3794d555514a2ba835e633875c2e07652ceb5889d57

Aliases

arxiv: 2605.09543 · arxiv_version: 2605.09543v1 · doi: 10.48550/arxiv.2605.09543 · pith_short_12: NGONV6TWSYK5 · pith_short_16: NGONV6TWSYK56IYY · pith_short_8: NGONV6TW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NGONV6TWSYK56IYY4N4U2VKVCS \
  | 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: 699cdafa769615df2318e3794d555514a2ba835e633875c2e07652ceb5889d57

Canonical record JSON

{
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    "abstract_canon_sha256": "927a4f6861b41e9673de7a1d455d53247bdf6557188cc4a7b1a2387be78e8dfe",
    "cross_cats_sorted": [
      "astro-ph.IM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "astro-ph.SR",
    "submitted_at": "2026-05-10T13:55:18Z",
    "title_canon_sha256": "8b688a3e726f1c96c408257a2c33f09d7f00cb8eaa557a7b8f7427fbda84d1c4"
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  "source": {
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    "kind": "arxiv",
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