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Paper Citation Record · LEDGER

Simulation-based inference of deep fields: galaxy population model and redshift distributions

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.06846.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.06846 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:18:10.841800Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T05:10:53.975138Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation dd36a713-8e95-4372-9c4b-c19a899bdb00 · inbound

Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI cites this paper.

Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI Simulation-based inference of deep fields: galaxy population model and redshift distributions

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-12T21:18:10.841800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:18:10.841800Z digest=sha256:48ba178112cf7389ee6d9d54416de5c15f9b5ed00246dea6a84aed06d372c803

Observation 7bb26f5c-e656-4e58-939e-3226b1fd8f40 · inbound

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts cites this paper.

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts Simulation-based inference of deep fields: galaxy population model and redshift distributions

Reference 131

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:10:53.976861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-11T00:51:26.783868Z digest=sha256:1e970f20fd2e91c4ad00f92f6cad5e896d649447984a5e0192a396ace543b9cf