Paper Citation Record · LEDGER

A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2407.21097.

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

pith.paper-citation-record.v1
2407.21097 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:33:47.960927Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7d382dc5-d637-499f-97e1-eb6ddb3f721b · inbound

Reproducibility of machine learning analyses of 21 cm reionization maps cites this paper.

Reproducibility of machine learning analyses of 21 cm reionization maps A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T11:05:34.306585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5674509e-d9f6-4c11-97e2-34768c935e80 · inbound

An Alcock-Paczynski Test on Reionization Bubbles for Cosmology cites this paper.

An Alcock-Paczynski Test on Reionization Bubbles for Cosmology A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T11:42:48.804215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6f22c85-b01e-4d7f-9961-ac548241c7b2 · inbound

Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation cites this paper.

Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T05:50:54.727334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 574286a7-37ba-4d95-8f85-0ac0c4c10a9d · inbound

Beyond the Power Spectrum: A New Framework for Non-Stationary Fields with Applications to Light-Cone Effects in Line Intensity Mapping cites this paper.

Beyond the Power Spectrum: A New Framework for Non-Stationary Fields with Applications to Light-Cone Effects in Line Intensity Mapping A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 117

Resolution: unresolved; no resolver link, observed 2026-08-15T21:33:47.960927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 264c7f56-3c59-4fa1-8cfe-ee5ed81d8f74 · inbound

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation cites this paper.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.089346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f487a33-ca13-4e95-9f69-d248d3a19376 · inbound

Seeing Wiggles without Seeing Wiggles: BAO Recovery in 21 cm Intensity Mapping with Deep Learning cites this paper.

Seeing Wiggles without Seeing Wiggles: BAO Recovery in 21 cm Intensity Mapping with Deep Learning A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T05:10:20.366129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ee5c236-b5d3-4088-869b-ba473aa19fdf · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-06-27T19:11:10.691895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.