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Paper Citation Record · LEDGER

Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2312.09943.

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

pith.paper-citation-record.v1
2312.09943 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:57:41.046768Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T20:06:50.304250Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
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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 ec180013-a931-4091-9a21-21f0d4c6cb54 · inbound

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation cites this paper.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T15:57:41.046768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:57:41.046768Z digest=sha256:8dde0fb2ac6c2f7234f65b596c67396cfc2f924ad8fc62b5e57224de342b2ffc

Observation f66651b5-0207-421d-a554-590feeddcdea · inbound

Searching for Inflationary Physics with the CMB Trispectrum: 1. Primordial Theory & Optimal Estimators cites this paper.

Searching for Inflationary Physics with the CMB Trispectrum: 1. Primordial Theory & Optimal Estimators Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 229

Resolution
unresolved
no resolver link, observed 2026-08-08T22:50:01.473787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T22:50:01.473787Z digest=sha256:dfa99a641d47692e2e9adc4c38b3c9d7227e420d3c00e5cdd5c786c3aa0bd2a8

Observation f9c3dd3e-6774-4202-8394-a4632f10c6e3 · inbound

Searching for Inflationary Physics with the CMB Trispectrum: 3. Constraints from Planck cites this paper.

Searching for Inflationary Physics with the CMB Trispectrum: 3. Constraints from Planck Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 114

Resolution
unresolved
no resolver link, observed 2026-08-08T14:22:19.795586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:22:19.795586Z digest=sha256:04e719cf1e5c9ecc301dedfe9e1364ee4dd05c34a3d02102bee035eca4119919

Observation 517c8c8d-065a-43a5-9923-475acb99760b · inbound

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach cites this paper.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-18T20:06:50.306861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-18T20:03:06.197589Z digest=sha256:b2e6f9c789798b7ed720d1edb1663e9f780acd3b3e62ea26a0f3cfc69c81b6d5