Pith Number

pith:SRSSLHOP

pith:2026:SRSSLHOPQN3LEDRP6EAJIMT4RS

not attested not anchored not stored refs resolved

Machine-learning applications for weak-lensing cosmology

Masato Shirasaki

Machine learning can help overcome limitations in traditional weak-lensing cosmology analyses.

arxiv:2605.12877 v1 · 2026-05-13 · astro-ph.CO · astro-ph.IM

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\usepackage{pith}
\pithnumber{SRSSLHOPQN3LEDRP6EAJIMT4RS}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Machine-learning techniques can mitigate the limitations inherent in traditional analyses and enhance the scientific return of current and upcoming weak-lensing datasets.

C2weakest assumption

That the machine learning approaches discussed in the literature are sufficiently mature, unbiased, and generalizable to real weak-lensing observations without introducing new systematic errors that offset their benefits.

C3one line summary

Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.

References

191 extracted · 191 resolved · 76 Pith anchors

[1] Steps towards nonlinear cluster inversion through gravitational distortions: III. Including a redshift distribution of the sources 1997 · arXiv:astro-ph/9601079

[2] The Three-Year Shear Catalog of the 2022 · doi:10.1093/pasj/psac006

[3] D. N. Limber, Astrophys. J.119, 655 (1954) doi:10.1086/145870 1954 · doi:10.1086/145870

[4] Analysis of two-point statistics of cosmic shear: I. Estimators and covariances 2002 · doi:10.1051/0004-6361:20021341

[5] 1993, ApJ, 404, 441, doi: 10.1086/172297 1993 · doi:10.1086/172297

Receipt and verification

First computed	2026-05-18T03:09:11.171831Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9465259dcf8376b20e2ff10094327c8c86d2587af4258ca6a04dc931196df476

Aliases

arxiv: 2605.12877 · arxiv_version: 2605.12877v1 · doi: 10.48550/arxiv.2605.12877 · pith_short_12: SRSSLHOPQN3L · pith_short_16: SRSSLHOPQN3LEDRP · pith_short_8: SRSSLHOP

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SRSSLHOPQN3LEDRP6EAJIMT4RS \
  | 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: 9465259dcf8376b20e2ff10094327c8c86d2587af4258ca6a04dc931196df476

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3014efb4b4b36facba8c3e7f0da46af8bc198249f54b678f23b756d0298f1ff1",
    "cross_cats_sorted": [
      "astro-ph.IM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "astro-ph.CO",
    "submitted_at": "2026-05-13T01:45:54Z",
    "title_canon_sha256": "418f3982d3d60e5e4ffe18851f2ddb9076d4817a5141cc94c3bfad3d78e376a8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12877",
    "kind": "arxiv",
    "version": 1
  }
}