Paper Citation Record · LEDGER

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution

As of 19 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 2 inbound Pith citation observations for arXiv:2411.11280.

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

pith.paper-citation-record.v1
2411.11280 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:48:42.633806Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T04:42:25.089569Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:06:53.112893Z

Reference resolution

19 of 19 outbound references displayed

verified exact5
verified fuzzy0
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fbd1bad-de48-4801-a66a-06ab39e240b3 · outbound

This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Optuna: A Next-generation Hyperparameter Optimization Framework

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.110105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d9541d1-d9a5-463a-acd1-62cf0fc29c84 · outbound

This paper cites A deep learning model to emulate simulations of cosmic reionization.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution A deep learning model to emulate simulations of cosmic reionization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.148922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f83422-36e1-4d88-b2ae-0aa4321e049d · outbound

This paper cites Cosmological constraints with deep learning from KiDS-450 weak lensing maps.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.282349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 573ada2c-f3c9-476e-9268-5ed0c4e4e120 · outbound

This paper cites Deep learning dark matter map reconstructions from DES SV weak lensing data.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Deep learning dark matter map reconstructions from DES SV weak lensing data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.389748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff6c9754-f514-4afa-a9ad-7118cf89ecf8 · outbound

This paper cites Model Comparison of Dark Energy models Using Deep Network.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Model Comparison of Dark Energy models Using Deep Network

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:48:43.008652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a00ffe78-5d92-4595-9af1-7a44f2b65cd3 · outbound

This paper cites An interpretable machine learning framework for dark matter halo formation.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution An interpretable machine learning framework for dark matter halo formation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.398430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34907a59-9c07-4ffe-9c64-a0ad8b89b076 · outbound

This paper cites CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T18:48:42.988172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da1c66cd-57eb-43fa-bd0e-237d4dab305a · outbound

This paper cites DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.410108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 482745ec-5e26-49fa-837f-865c5fa2c0e6 · outbound

This paper cites Fast Wiener filtering of CMB maps with Neural Networks.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Fast Wiener filtering of CMB maps with Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.413909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6385f59a-29e2-4daa-93e2-171028a8cc66 · outbound

This paper cites The Role of Machine Learning in the Next Decade of Cosmology.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution The Role of Machine Learning in the Next Decade of Cosmology

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.416919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2288ef94-2843-4b60-bc6f-344ebdd47956 · outbound

This paper cites Deconfusing intensity maps with neural networks.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Deconfusing intensity maps with neural networks

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:48:42.884657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c671c31-3d4f-4d48-a057-06b1d6ffd8cc · outbound

This paper cites Cosmological model discrimination with Deep Learning.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Cosmological model discrimination with Deep Learning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:48:42.780994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 018d2ad5-ea06-4450-881d-9b13674ea85f · outbound

This paper cites Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:48:42.693955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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

This paper cites From Dark Matter to Galaxies with Convolutional Networks.

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 f3e36c71-44a0-4334-995a-c4784993e630 · outbound

This paper cites Estimating Cosmological Parameters from the Dark Matter Distribution.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Estimating Cosmological Parameters from the Dark Matter Distribution

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.424346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f665d5-6750-4e7e-8677-be45597e29c1 · outbound

This paper cites Improved Photometric Classification of Supernovae using Deep Learning.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Improved Photometric Classification of Supernovae using Deep Learning

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.406139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f71f6f3-40ca-4a2d-ad16-0f4709b092f2 · outbound

This paper cites Machine learning and the physical sciences.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Machine learning and the physical sciences

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.115667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aadce56-065e-49d2-92a9-48007861d93d · outbound

This paper cites Velocity recostruction with graph neural networks.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Velocity recostruction with graph neural networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:42.587118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ead24c5-057a-4a1a-be77-60a3fc2c2354 · outbound

This paper cites 2015, arXiv e-prints, arXiv:1503.03757.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution 2015, arXiv e-prints, arXiv:1503.03757

Reference 2025

Resolution: verified exact; raw_fallback, observed 2026-08-12T18:48:42.768510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 8e2759c8-1ce3-4a7f-b167-c4f8f7571b91 · inbound

Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction cites this paper.

Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-21T02:53:55.219901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55c7323b-d95f-4902-a9a3-b06b2e153d8a · inbound

Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning cites this paper.

Full Nonlinear Velocity Reconstruction With Transformer and Ensemble Tree Machine Learning AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:06:53.114382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.