Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:16:02.883011Z
Paper Citation Record · LEDGER
Cosmological N-body simulations: a challenge for scalable generative models
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:1908.05519.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T20:14:12.879470Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T16:45:51.764712Z
Reference resolution
63 of 63 outbound references displayed
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External citation measurements
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Outbound references
Observation d55e87a8-937f-4ce2-aace-1dc246e783f1 · outbound
This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 1
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This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 2
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Observation e754c600-f440-42d6-90f3-620f15688650 · outbound
This paper cites Simulating the joint evolution of quasars, galaxies and their large-scale distribution.
Cosmological N-body simulations: a challenge for scalable generative models Simulating the joint evolution of quasars, galaxies and their large-scale distribution
Reference 3
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This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 4
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Observation 33d869fc-1223-44e9-91fe-386dbe97bc93 · outbound
This paper cites Numerical Simulations of the Dark Universe: State of the Art and the Next Decade.
Cosmological N-body simulations: a challenge for scalable generative models Numerical Simulations of the Dark Universe: State of the Art and the Next Decade
Reference 5
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Observation efffadcf-b413-4549-8215-08d42b6f08df · outbound
This paper cites Computational Astrophysics and Cosmology 5(1), 4 (2018).
Cosmological N-body simulations: a challenge for scalable generative models Computational Astrophysics and Cosmology 5(1), 4 (2018)
Reference 6
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Observation 726bec30-d8a4-4f15-ae2e-e65ae2fee6da · outbound
This paper cites CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks.
Cosmological N-body simulations: a challenge for scalable generative models CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Reference 7
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 8
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 11
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This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 12
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 13
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 14
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Observation cccf1758-2de2-4663-86bf-99dca7cbaba9 · outbound
This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
Reference 15
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Observation 571263e1-e770-4a4c-abd0-f410a4d0a7a5 · outbound
This paper cites In: Advances in Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances in Neural Information Processing Systems, pp
Reference 16
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Observation 59bd20fc-4891-49c9-80ab-040a8d68ae8e · outbound
This paper cites Physical Review E 96(4), 043309 (2017).
Cosmological N-body simulations: a challenge for scalable generative models Physical Review E 96(4), 043309 (2017)
Reference 17
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Observation 141341c7-6412-4381-8357-5dac69e2dae0 · outbound
This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 18
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Observation 8e929f8e-d9dc-49e4-ad39-bf84f862652f · outbound
This paper cites : Photo-realistic single image super-resolution using a generative adversarial network.
Cosmological N-body simulations: a challenge for scalable generative models : Photo-realistic single image super-resolution using a generative adversarial network
Reference 19
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Observation 909ff81b-a769-4c38-8df0-c602aeced87f · outbound
This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
Reference 20
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Observation ec43ad78-1124-445c-9e0c-0483e26b6749 · outbound
This paper cites In: Proceedings of the European Conference on Computer Vision (ECCV), pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Proceedings of the European Conference on Computer Vision (ECCV), pp
Reference 21
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 22
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Observation 87e02048-33f8-4b7e-a691-65550909087e · outbound
This paper cites Dark Energy Survey Year 1 Results: Curved-Sky Weak Lensing Mass Map.
Cosmological N-body simulations: a challenge for scalable generative models Dark Energy Survey Year 1 Results: Curved-Sky Weak Lensing Mass Map
Reference 24
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Cosmological N-body simulations: a challenge for scalable generative models Minkowski Functionals in Cosmology
Reference 25
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This paper cites Learning to Predict the Cosmological Structure Formation.
Cosmological N-body simulations: a challenge for scalable generative models Learning to Predict the Cosmological Structure Formation
Reference 26
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Observation c837d96a-082c-46ba-9137-a141f546a802 · outbound
This paper cites Painting with baryons: augmenting N-body simulations with gas using deep generative models.
Cosmological N-body simulations: a challenge for scalable generative models Painting with baryons: augmenting N-body simulations with gas using deep generative models
Reference 28
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Cosmological N-body simulations: a challenge for scalable generative models Deblending galaxy superpositions with branched generative adversarial networks
Reference 30
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This paper cites Painting halos from cosmic density fields of dark matter with physically motivated neural networks.
Cosmological N-body simulations: a challenge for scalable generative models Painting halos from cosmic density fields of dark matter with physically motivated neural networks
Reference 31
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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This paper cites : Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing.
Cosmological N-body simulations: a challenge for scalable generative models : Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing
Reference 34
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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This paper cites Galaxy lensing mocks from all-sky lensing maps (2015).
Cosmological N-body simulations: a challenge for scalable generative models Galaxy lensing mocks from all-sky lensing maps (2015)
Reference 38
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
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Observation 00982f65-537f-4e08-9a9f-74a4b82b3afa · outbound
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Cosmological N-body simulations: a challenge for scalable generative models Matter power spectrum and the challenge of percent accuracy
Reference 42
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Source-reported events for the cited work
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Observation 52ec5834-c422-48f4-ad81-abbb8b806ba0 · outbound
This paper cites An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models.
Cosmological N-body simulations: a challenge for scalable generative models An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models
Reference 43
- Resolution
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Source-reported events for the cited work
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Observation 9925e02c-f9ad-485d-844e-2581787adfdc · outbound
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Cosmological N-body simulations: a challenge for scalable generative models Modeling baryonic physics in future weak lensing surveys
Reference 44
- Resolution
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Source-reported events for the cited work
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Observation 27e0e923-73bd-4557-b9ee-4b59e45aa9a2 · outbound
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Cosmological N-body simulations: a challenge for scalable generative models Separate Universe Simulations with IllustrisTNG: baryonic effects on power spectrum responses and higher-order statistics
Reference 45
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f548295-4dd4-4033-a79b-728703fc8c50 · outbound
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Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 46
- Resolution
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Observation 39fab19f-5ecf-4bbf-bfac-16fb3bd5ed57 · outbound
This paper cites Computer Vision and Image Understanding 179, 41–65 (2019).
Cosmological N-body simulations: a challenge for scalable generative models Computer Vision and Image Understanding 179, 41–65 (2019)
Reference 47
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d4ad97d-0883-4ee7-927f-26f7d3220c21 · outbound
This paper cites In: Advances in Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances in Neural Information Processing Systems, pp
Reference 48
- Resolution
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Observation 889585e8-5fee-4246-a81a-7526a6f9c17e · outbound
This paper cites : Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data.
Cosmological N-body simulations: a challenge for scalable generative models : Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data
Reference 49
- Resolution
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Source-reported events for the cited work
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Observation 15a9c8eb-3ada-47ce-94c6-1f428f33b9ae · outbound
This paper cites : KiDS-450: cosmological constraints from weak-lensing peak statistics–II: Inference from shear peaks using N-body simulations.
Cosmological N-body simulations: a challenge for scalable generative models : KiDS-450: cosmological constraints from weak-lensing peak statistics–II: Inference from shear peaks using N-body simulations
Reference 50
- Resolution
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Source-reported events for the cited work
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Observation a532d9ce-b3c4-4ff6-9b8c-6955b3b60b72 · outbound
This paper cites In: Advances in Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances in Neural Information Processing Systems, pp
Reference 51
- Resolution
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Source-reported events for the cited work
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Observation 094fa7e5-5213-4346-9d6a-c056fff67207 · outbound
This paper cites Comptes rendus hebdomadaires des s´ eances de l’acad´ emie des sciences244(6), 689–692 (1957).
Cosmological N-body simulations: a challenge for scalable generative models Comptes rendus hebdomadaires des s´ eances de l’acad´ emie des sciences244(6), 689–692 (1957)
Reference 52
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d42df8b9-a1b2-4ea0-bcba-1df1ca8212a8 · outbound
This paper cites Journal of multivariate analysis 12(3), 450–455 (1982).
Cosmological N-body simulations: a challenge for scalable generative models Journal of multivariate analysis 12(3), 450–455 (1982)
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:04.065083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5b283513-f69b-4825-b4a8-b206d1665471 · outbound
This paper cites In: Advances in Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances in Neural Information Processing Systems, pp
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:04.003261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8ddc97eb-cc5c-4e4e-a20d-8d0cbcb95b5a · outbound
This paper cites In: Advances in Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances in Neural Information Processing Systems, pp
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:03.920904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7afe57ba-f120-4c54-a9e2-3a3de57af62c · outbound
This paper cites In: International Conference on Machine Learning, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: International Conference on Machine Learning, pp
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:03.884734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f6afa8e9-d1e5-4743-a048-326745f91c62 · outbound
This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 57
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T13:16:03.849108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4caa81c2-ea69-4495-9154-28f56b768919 · outbound
This paper cites In: Advances In Neural Information Processing Systems, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Advances In Neural Information Processing Systems, pp
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:03.794644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d0110217-8801-4869-80af-b43bad44a4ce · outbound
This paper cites In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018).
Cosmological N-body simulations: a challenge for scalable generative models In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:03.724412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0033375a-f669-4df2-8e94-49831dd940dd · outbound
This paper cites In: Proceedings of the IEEE International Conference on Computer Vision, pp.
Cosmological N-body simulations: a challenge for scalable generative models In: Proceedings of the IEEE International Conference on Computer Vision, pp
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:16:03.694980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6e1a39b0-0eb6-4e67-88b3-9a600beb70a6 · outbound
This paper cites Conditional Generative Adversarial Nets.
Cosmological N-body simulations: a challenge for scalable generative models Conditional Generative Adversarial Nets
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:16:02.851247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f26848a-333c-4814-b52a-81716261acc4 · outbound
This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 62
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T13:16:03.661470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bee2fa5b-8929-4d68-accb-85c438cd8b11 · outbound
This paper cites an unresolved cited work.
Cosmological N-body simulations: a challenge for scalable generative models Unresolved cited work
Reference 63
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T13:16:03.637262Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
Observation 1e915e91-a9f7-46af-a87c-1da96d98108a · inbound
Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps cites this paper.
Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps Cosmological N-body simulations: a challenge for scalable generative models
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T16:45:51.766105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.