Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.12724.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:30:55.604036Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation abaaaea9-89ba-47d1-9210-df87d7f3fb82 · inbound
Generating Dark Matter Subhalo Populations Using Normalizing Flows cites this paper.
Generating Dark Matter Subhalo Populations Using Normalizing Flows HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:30:55.604036Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2cc3bbc-d858-4a68-9d12-8961779aa8c3 · inbound
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective cites this paper.
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
Reference 275
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:46:14.865198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 25db59ed-91cf-4898-a8d9-af38a740f6b8 · inbound
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
Reference 39
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-27T19:11:10.732652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.