Paper Citation Record · LEDGER

Using machine learning to parametrize postmerger signals from binary neutron stars

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2201.06461.

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

pith.paper-citation-record.v1
2201.06461 v1

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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:20:01.478485Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:54:45.318194Z

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 25fb7105-ae6a-4ec2-9c05-3af4c01e3d55 · inbound

Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders cites this paper.

Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders Using machine learning to parametrize postmerger signals from binary neutron stars

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T05:20:01.478485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ab00688-ebe7-4f0f-a487-e427169c353c · inbound

Parameter inference of millilensed gravitational waves using neural spline flows cites this paper.

Parameter inference of millilensed gravitational waves using neural spline flows Using machine learning to parametrize postmerger signals from binary neutron stars

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:22:18.770790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8c7ea557-a4f6-4d22-a036-32a75c75411d · inbound

Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks cites this paper.

Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks Using machine learning to parametrize postmerger signals from binary neutron stars

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:54:45.319586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 72457b65-4c7e-404c-89ff-d8cb8486a511 · inbound

Identifying lensed gravitational waves with physics-informed posterior learning cites this paper.

Identifying lensed gravitational waves with physics-informed posterior learning Using machine learning to parametrize postmerger signals from binary neutron stars

Reference 126

Resolution: unresolved; no resolver link, observed 2026-07-11T23:16:00.672720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.