Paper Citation Record · LEDGER

CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1706.02390.

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

pith.paper-citation-record.v1
1706.02390 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:16:02.200246Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-18T20:06:50.233820Z

Reference resolution

0 of 0 outbound references displayed

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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 726bec30-d8a4-4f15-ae2e-e65ae2fee6da · inbound

Cosmological N-body simulations: a challenge for scalable generative models cites this paper.

Cosmological N-body simulations: a challenge for scalable generative models CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T13:16:02.200246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fbdf1f4-1e1d-40cd-bb03-cf5baaa64e8d · inbound

Diffusion-based mass map reconstruction from weak lensing data cites this paper.

Diffusion-based mass map reconstruction from weak lensing data CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T23:21:45.013839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7567ab20-8eeb-4ac5-971f-5a07237a8e54 · inbound

Leveraging GNN to Enhance MEF Method in Predicting ENSO cites this paper.

Leveraging GNN to Enhance MEF Method in Predicting ENSO CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T22:12:02.851413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ec291af-d541-4087-81b7-a8d5937b1dce · inbound

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach cites this paper.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:06:50.236588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e48f4a92-723e-414a-8b75-3d81d20117fd · inbound

Replicating weak-lensing summary-statistic covariances with normalizing flows cites this paper.

Replicating weak-lensing summary-statistic covariances with normalizing flows CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T07:22:26.030322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488f7cad-4f80-4de5-b5a1-b3c2386250ec · inbound

Machine-learning applications for weak-lensing cosmology cites this paper.

Machine-learning applications for weak-lensing cosmology CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 180

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:07:50.005071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 85df1804-b5c1-44d6-955f-7ba5a0fdfa15 · inbound

Fast(er)PM and Moving Mesh: JAX-native Geometric Multigrid Methods cites this paper.

Fast(er)PM and Moving Mesh: JAX-native Geometric Multigrid Methods CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 189

Resolution: unresolved; no resolver link, observed 2026-07-14T07:52:28.510358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.