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Paper Citation Record · LEDGER

Correcting for interloper contamination in the power spectrum with neural networks

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2504.06919.

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

pith.paper-citation-record.v1
2504.06919 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:49:00.805426Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T16:14:15.137392Z

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 c9cde721-59ed-446e-b282-326dbe991177 · inbound

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery cites this paper.

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery Correcting for interloper contamination in the power spectrum with neural networks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:00.805426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:00.805426Z digest=sha256:2619fb0479409e0f05818e4f14a8ca7c0206fea16f3fd7c0dc501533a60b45f5

Observation cafdf140-3efb-4d9a-8163-3b27ef7fefac · inbound

Disentangling Target Lines from Interlopers and Continuum with Neural Networks: A SPHEREx Intensity Mapping Case Study cites this paper.

Disentangling Target Lines from Interlopers and Continuum with Neural Networks: A SPHEREx Intensity Mapping Case Study Correcting for interloper contamination in the power spectrum with neural networks

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T11:41:18.246353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:41:18.246353Z digest=sha256:3a402ceeebed83b2b08a4ec738f66dd2ff4c87a89d16871815eebeee50e27b00

Observation c95cd35e-7f4f-4199-9bb0-f37a4d969fcc · inbound

Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy cites this paper.

Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy Correcting for interloper contamination in the power spectrum with neural networks

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-21T16:14:15.139093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T16:12:23.173655Z digest=sha256:3d3f112b52d9c9b7d1ea115877b03ca0e82a2fb0b8e77cd7e1e2f9cabf90fd5a