Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2202.07158.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T15:57:43.274800Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T08:05:30.951793Z
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 b374a8a7-6cd2-4dd3-bc85-787fea6fffeb · inbound
Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh cites this paper.
Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:57:43.274800Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91fdd36a-9d6e-4b4c-988f-f11318b01e30 · inbound
Sequential simulation-based inference for extreme mass ratio inspirals cites this paper.
Sequential simulation-based inference for extreme mass ratio inspirals Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
Reference 92
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:59:16.638150Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e40d7cfa-3050-4934-8ab1-41ba4a268cd3 · inbound
Searching for extreme mass ratio inspirals in LISA: from identification to parameter estimation cites this paper.
Searching for extreme mass ratio inspirals in LISA: from identification to parameter estimation Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:43:24.160550Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d082261b-7904-4ca0-bebe-5d9fe6da4606 · inbound
Global time-frequency search for stellar-mass binary black holes in LISA cites this paper.
Global time-frequency search for stellar-mass binary black holes in LISA Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T08:05:30.954217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0bd81de2-cbea-439e-9572-a1bd71aa2d03 · inbound
Pre-localization of Massive Black Hole Binaries in the Millihertz Band cites this paper.
Pre-localization of Massive Black Hole Binaries in the Millihertz Band Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T22:51:22.544487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.