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Paper Citation Record · LEDGER

Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.15559.

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

pith.paper-citation-record.v1
2411.15559 v1

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:10:40.169273Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:10:40.321639Z

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 591a554d-8036-4699-8e5d-3a5ab41a5240 · inbound

Simulating realistic radio continuum survey maps with diffusion models cites this paper.

Simulating realistic radio continuum survey maps with diffusion models Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation

Reference 16

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T04:10:40.326595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T04:10:40.169273Z digest=sha256:3904b9365cf53416c3e7ecc1a4bfd7859e8f35b38740908f35d234e82b3714b8

Observation cca780d3-8648-4342-b177-cce61f3a01e6 · inbound

A Guided Unconditional Diffusion Model to Synthesize and Inpaint Radio Galaxies from FIRST, MGCLS and Radio Zoo cites this paper.

A Guided Unconditional Diffusion Model to Synthesize and Inpaint Radio Galaxies from FIRST, MGCLS and Radio Zoo Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-03T11:08:08.102775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:08:08.102775Z digest=sha256:9deaeb044d430fa31314bfbb5a46c66d0ee91460ed5880d80ca6ea7e43f75e1f