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Paper Citation Record · LEDGER

Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.00018.

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

pith.paper-citation-record.v1
2107.00018 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:15:32.178662Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 48bddef6-4c8b-4d7f-b6f7-b237744c6f34 · inbound

CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization cites this paper.

CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-04T13:15:32.178662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2ddd1770-3fec-4c31-8834-689be6034608 · 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 Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-03T05:10:20.063699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:10:20.063699Z digest=sha256:d1380c7ec6f8ae9853074dbe1977860948055b4820a2765d2686e3bdfbb170c1