Paper Citation Record · LEDGER

From Dark Matter to Galaxies with Convolutional Networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1902.05965.

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

pith.paper-citation-record.v1
1902.05965 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:05:14.777170Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

41
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 43ced4b2-e245-4ea9-93c7-8b6da918aef0 · inbound

Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations cites this paper.

Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations From Dark Matter to Galaxies with Convolutional Networks

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-14T11:05:14.777170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6215e67-bbd6-47d9-b723-73890f3548a1 · inbound

Cosmological parameter estimation from large-scale structure deep learning cites this paper.

Cosmological parameter estimation from large-scale structure deep learning From Dark Matter to Galaxies with Convolutional Networks

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-14T10:47:27.980850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b52e15c4-f0af-4450-b249-5f5228ce98e3 · inbound

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution cites this paper.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution From Dark Matter to Galaxies with Convolutional Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.633806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d4eccf-e8d9-4d3b-8f5c-0382afe9f476 · inbound

Probabilistic Galaxy Field Generation with Diffusion Models cites this paper.

Probabilistic Galaxy Field Generation with Diffusion Models From Dark Matter to Galaxies with Convolutional Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:51.536853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 576ee8d0-2f30-46ff-8fdf-f3b233fe2859 · inbound

JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen maps from fast N-body simulations cites this paper.

JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen maps from fast N-body simulations From Dark Matter to Galaxies with Convolutional Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T20:15:39.544862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5088915b-d335-4476-acf9-a4ec8738f502 · inbound

From Dark Matter to Galaxies: Halo-Free Mock Generation via Conditional Point-Cloud Diffusion cites this paper.

From Dark Matter to Galaxies: Halo-Free Mock Generation via Conditional Point-Cloud Diffusion From Dark Matter to Galaxies with Convolutional Networks

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-01T11:48:29.022234Z

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.