{"as_of":"2026-08-16T14:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0bd134f165afb2d46fbfbf3e632fbb703d55300cee29304a610511affe95f92f","coverage":[{"denominator":167,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:24:36.165071Z","state":"measured"},{"denominator":107,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":107,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T04:47:57.349838Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T22:23:59.927409Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-08-12T04:47:57.349838Z","title":"III–1139 Svinkin D","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.01150","last_updated":"2025-03-04T04:31:11Z","snapshot_observed_at":"2026-08-14T03:01:17.186109Z","submitted_at":"2024-12-02T05:48:31Z","title":"Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T04:47:57.349838Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2412.01150"},"observation_digest":"sha256:075a3eec3deeab8e6498cfe335ff15e7469a41acf8f816d66f7153037e5c852f","observation_id":"a12d2472-83c0-4d2a-9d8b-37539d86f502","resolution":{"observed_at":"2026-08-12T04:47:57.349838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-08-06T20:22:39.455522Z","title":"In: The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, ://arxiv.org/abs/2412.02527, 2412.02527","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.03093","last_updated":"2025-07-03T18:05:40Z","snapshot_observed_at":"2026-08-14T20:43:14.334607Z","submitted_at":"2025-07-03T18:05:40Z","title":"From stellar light to astrophysical insight: automating variable star research with machine learning","version":1},"reference_index":171,"source":"arxiv_source","source_observed_at":"2026-08-06T20:22:39.455522Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2507.03093"},"observation_digest":"sha256:fd60533f6ba5ffdf428e64604f03b60eac6b6ce0770b594d42f731dbdedb62ed","observation_id":"9d941a1c-cf49-4180-b44b-fc1bfc549f56","resolution":{"observed_at":"2026-08-06T20:22:39.455522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-08-06T19:32:22.716816Z","title":"Zhang, G., Helfer, T., Gagliano, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05333","last_updated":"2025-07-07T18:00:00Z","snapshot_observed_at":"2026-08-14T06:39:32.761131Z","submitted_at":"2025-07-07T18:00:00Z","title":"Causal Foundation Models: Disentangling Physics from Instrument Properties","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:32:22.716816Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2507.05333"},"observation_digest":"sha256:84f7975507444dc2f5d7c4baa880e1d03deaf20c614b6846c9a3e62d4d43bd2d","observation_id":"88461b21-1356-4b71-92de-f64c1726b876","resolution":{"observed_at":"2026-08-06T19:32:22.716816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-07-12T23:05:33.283641Z","title":"The multimodal universe: Enabling large-scale machine learning with 100tb of astronomical scientific data, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.09787","last_updated":"2026-06-06T19:38:07Z","snapshot_observed_at":"2026-08-15T02:08:07.471615Z","submitted_at":"2026-04-10T18:11:05Z","title":"Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-12T23:05:33.283641Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2604.09787"},"observation_digest":"sha256:2170a4b2490971401b9f7602b666f7668f5dc4a7b4687a38b8a430ec55508f9d","observation_id":"9bcf891a-c5b9-4a75-b92e-059ffa094fbc","resolution":{"observed_at":"2026-07-12T23:05:33.283641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":"2412.02527","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-06-29T22:23:59.927409Z","title":"The multimodal universe: enabling large-scale machine learning with 100tb of astronomical scientific data,","venue":null,"work_id":"b2956be1-0f16-4fd7-a182-9037da4a6281","year":2024},"citing_paper":{"arxiv_id":"2605.23136","last_updated":"2026-05-22T01:19:03Z","snapshot_observed_at":"2026-08-15T11:12:04.279044Z","submitted_at":"2026-05-22T01:19:03Z","title":"A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T03:38:34.829999Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2605.23136"},"observation_digest":"sha256:059779b4a9f35b576296d78c2cf07732c1bc4cc6837e3c83a304309a86edbbbb","observation_id":"93a7d488-dbb7-4cd8-a5d4-b3f72b497b4b","resolution":{"observed_at":"2026-05-25T03:40:18.301859Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":"2412.02527","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-06-29T22:23:59.927409Z","title":"The multimodal universe: enabling large-scale machine learning with 100tb of astronomical scientific data,","venue":null,"work_id":"b2956be1-0f16-4fd7-a182-9037da4a6281","year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-07-06T23:36:11.031436Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:bf6afc452a53e7c7adad62bf5db9c02e7e9adda266b533f6a956759c695ef6fb","observation_id":"c41e6977-cc42-47c3-a1e4-99e6663221a4","resolution":{"observed_at":"2026-06-29T22:23:59.929114Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-08-01T09:25:42.186226Z","title":"2024, arXiv e-prints, arXiv:2412.02527, doi: 10.48550/arXiv.2412.02527 van den Oord, A., Li, Y., & Vinyals, O","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20797","last_updated":"2026-07-22T23:54:49Z","snapshot_observed_at":"2026-08-14T22:44:35.084225Z","submitted_at":"2026-07-22T23:54:49Z","title":"A Multimodal Approach to Star--Galaxy Separation using SPHEREx Spectrophotometry and DESI Legacy Survey Imaging","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:42.186226Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2607.20797"},"observation_digest":"sha256:d26da722fd873e8cc535189a0584753badbc93bae1b4371f5ef19ee33ddf3973","observation_id":"a07b3d6d-1c0e-4684-976e-7c8b28fa2939","resolution":{"observed_at":"2026-08-01T09:25:42.186226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02527/citation-record","integrity":"/paper/2412.02527/integrity","json":"/paper/2412.02527/citation-record.json","paper":"/paper/2412.02527"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.667068Z","title":"Abazajian, Jennifer K","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.667068Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e00476ed4ed957083006128b4a7797158494961d97a2bd8a704f93c8b062fa46","observation_id":"b1da2ea4-b557-48c8-b683-42d3ac8a817c","resolution":{"observed_at":"2026-08-11T23:24:35.667068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.673645Z","title":"Filiz Ak, Shadab Alam, Carlos Allende Prieto, Andrés Almeida, Friedrich Anders, Scott F","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.673645Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:831de6cc956170b2dc24d2e8e42d892c6c88829a5e5a6f551d485a1215c9f35c","observation_id":"3ed7570c-8a15-4569-9014-18c3283b7003","resolution":{"observed_at":"2026-08-11T23:24:35.673645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.679153Z","title":"Lupton, Nate B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.679153Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:357e2974cd5c350c4a66705ffea2a2ce7e53bb3dd90bb0df69e67e40db96b862","observation_id":"92732334-f254-4fb3-a0d7-cc4064ac3378","resolution":{"observed_at":"2026-08-11T23:24:35.679153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.684239Z","title":"Lupton, Nate B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.684239Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:02dd8936927218825f88ca40afc3d454d8731d3890c385de478dae2cf0831c73","observation_id":"46aa2880-4c08-4b25-9474-271a879a85b0","resolution":{"observed_at":"2026-08-11T23:24:35.684239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.689163Z","title":"Bah- call, Steven Bickerton, James Bosch, Kevin Bundy, Peter L","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.689163Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:ce2e7733ab6aa469485a46fa758b46aad1684cb6308830b671b1b7b7d7c3d567","observation_id":"522cc426-cee5-4d08-9cd6-ab50ca5e615a","resolution":{"observed_at":"2026-08-11T23:24:35.689163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.694148Z","title":"Albareti, Carlos Allende Prieto, F","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.694148Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:aa548ba1e5d2adf65f3b564d7cc2a8aa5cbacddc2de599fec12d4775b044efd2","observation_id":"4401a989-f887-43cb-803b-3c8a5e67d366","resolution":{"observed_at":"2026-08-11T23:24:35.694148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.700048Z","title":null,"venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.700048Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:3ffcc2174aff1c72f2d17eb699edd7b4512896ec0baf6532e4b7744d64fb97a3","observation_id":"f88f8c57-7885-44ac-bd36-41346843609c","resolution":{"observed_at":"2026-08-11T23:24:35.700048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.705097Z","title":"Alves, Hiranya V","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.705097Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:fde414af47559bf1a3852f3cbb53bee4070534c2eb76d4b6ed258d3fa660fc80","observation_id":"50f44c26-48b5-48f3-a951-549b352f5df6","resolution":{"observed_at":"2026-08-11T23:24:35.705097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.709759Z","title":"Amanullah, C","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.709759Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f61b6d8448767fd6fec10992a37d67b4007493666413e2b1ceca3b16a0077959","observation_id":"2d35d8c2-5238-4c0f-8917-51e613ac6885","resolution":{"observed_at":"2026-08-11T23:24:35.709759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.714677Z","title":"Galaxy zoo - the galaxy challenge, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.714677Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:9a05be961a8a6729065c72a66fa6346bb36d2415f1cbdee5c2876f7540cf9813","observation_id":"d7b142d1-74dc-4e85-a3c5-135d138e178e","resolution":{"observed_at":"2026-08-11T23:24:35.714677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.719397Z","title":"Audenaert, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.719397Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:778ca8a94f98694748a31a29ce7ff09c54a6e19b2aa1d3f94bf3efbd24f175e2","observation_id":"d8dcaaa2-6df6-44e2-bf23-fdcc99fffc04","resolution":{"observed_at":"2026-08-11T23:24:35.719397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.724047Z","title":"Audenaert and A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.724047Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b38dc71d4a922df4aa203a4f8ae0f1f126083f68f527ea733d2436dc75149109","observation_id":"769446eb-90b6-4f38-bfb2-c54a911d0f1a","resolution":{"observed_at":"2026-08-11T23:24:35.724047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.728748Z","title":"Bagley, Steven L","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.728748Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a9c27b5f67f52f0bba810ca7a3fcad65cd80c7087b82dded8fa9f267910fcbb1","observation_id":"6b57c806-71ad-4f2e-9ea8-657891505f9f","resolution":{"observed_at":"2026-08-11T23:24:35.728748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.734132Z","title":"Bagley, Nor Pirzkal, Steven L","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.734132Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:66ac20ab34c1cd5ee0ae92d4544f7f6bf896ad8cd4e2808f79c7e6233a5b6d30","observation_id":"eb65be18-ec27-4e36-b05d-aa9d23501018","resolution":{"observed_at":"2026-08-11T23:24:35.734132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.739107Z","title":"Bellm, Shrinivas R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.739107Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:ea540fe743751960146563dd4d7da77f84bccf5250b73dc65f14fa83c1a87353","observation_id":"a0e4877e-e08a-46c4-a3ab-3f35c945579f","resolution":{"observed_at":"2026-08-11T23:24:35.739107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.743978Z","title":"Bellm, Shrinivas R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.743978Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:4692a5a23b963bb6e82d971d670be61aa33332a60dc66e04333eebbc40ab0d47","observation_id":"b18c756a-a92d-4143-887d-61104eff2246","resolution":{"observed_at":"2026-08-11T23:24:35.743978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.748794Z","title":"Betoule, R","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.748794Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a051da3a74b6765da9c14b20aeec04aa51aab4ec80a0e28e8a0d97497ee92d87","observation_id":"3b3a48c7-77b9-46be-ae3a-21fcc0480636","resolution":{"observed_at":"2026-08-11T23:24:35.748794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.753639Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.753639Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:5b63dcc9271c2efd4d6bcc9a53c1e28ca2deb61450bcce852a923ac20f98bc80","observation_id":"2580c94a-cd8f-4ca9-89fa-78be8ef1976c","resolution":{"observed_at":"2026-08-11T23:24:35.753639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.759070Z","title":"Blanton, Matthew A","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.759070Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:5a8a05b0241fa7daf99a0649905271a51292a8c23c8de5feb9c0ddbc1a76f1ef","observation_id":"ad1b81bf-9df6-4a22-8042-6aa045fdb2c9","resolution":{"observed_at":"2026-08-11T23:24:35.759070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.763957Z","title":"A vocado: Photometric classification of astronomical transients with gaussian process augmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.763957Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:025f38669707c14f81a4b06c4bc876524cd5a94425b4b695e8c58dab22514e63","observation_id":"e02c06e8-8436-49b3-959c-853ae1804ee1","resolution":{"observed_at":"2026-08-11T23:24:35.763957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.768678Z","title":"Borucki, David Koch, Gibor Basri, Natalie Batalha, Timothy Brown, Dou- glas Caldwell, John Caldwell, Jørgen Christensen-Dalsgaard, William D","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.768678Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:1735c010bbd8e58f95fed7f8ccea537ef6fa265579f7f461c9720228cee9ba0d","observation_id":"abb2e6c0-0a96-4353-b9db-3d28eea13bd1","resolution":{"observed_at":"2026-08-11T23:24:35.768678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.773458Z","title":"Radio galaxy zoo EMU: towards a semantic radio galaxy morphology taxonomy","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.773458Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:c585ee55f837f0607da498348caa0531f91dcb65082ccf3790866f95fe2e526d","observation_id":"70d0ea48-8caf-49a5-a0e4-ef46053966a0","resolution":{"observed_at":"2026-08-11T23:24:35.773458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14760","last_updated":"2022-10-27T09:10:34Z","snapshot_observed_at":"2026-08-13T13:55:57.753559Z","submitted_at":"2022-10-26T14:48:50Z","title":"A New Task: Deriving Semantic Class Targets for the Physical Sciences","version":2},"cited_work":{"arxiv_id":"2210.14760","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.14760","snapshot_observed_at":"2026-08-11T23:24:37.070863Z","title":"A New Task: Deriving Semantic Class Targets for the Physical Sciences","venue":"astro-ph.IM","work_id":"ae9ae6b1-ac2d-43ff-8ee9-323f5778523f","year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.778087Z"},"links":{"cited_paper":"/paper/2210.14760","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b12a6263f7353c5fd6717262557880c440dff6b6a4d57fe3b3799991dc714d83","observation_id":"a48c602f-8b6e-47e2-a111-c2830824a126","resolution":{"observed_at":"2026-08-11T23:24:37.076423Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.783279Z","title":"Brout, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.783279Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:9fc78e40cf12d12973371c278e5a08357819bff105df1c5c300b0417c647d335","observation_id":"a728a92b-93fa-45b0-a7e9-9d750cc6cdec","resolution":{"observed_at":"2026-08-11T23:24:35.783279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.787898Z","title":"Brout, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.787898Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f97cfa4d9022998bb5f733061089ccfde04cb639feb1025712a2c6437551f84d","observation_id":"e8230fbc-1f87-403a-9364-121b2ca27fb8","resolution":{"observed_at":"2026-08-11T23:24:35.787898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.797744Z","title":"Brown, Alice A","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.797744Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e0a5cc2b07cead841d7312050e4a462a5a909e9439ea38a077a3d8d9cfddd2d9","observation_id":"bf98598a-fbbc-4722-8964-b94c2e5656b1","resolution":{"observed_at":"2026-08-11T23:24:35.797744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.802346Z","title":"Amarsi, Thomas Nordlander, Karin Lind, Sarah L","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.802346Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:0d9b3f4f26badf1bd4082af137e9f580dc23577f22b609c10ccbf3ca47f429ec","observation_id":"b126debe-fda6-4930-bf0d-98d772fbbb19","resolution":{"observed_at":"2026-08-11T23:24:35.802346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.807081Z","title":"Bershady, David R","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.807081Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:951a38531cf07917f571492812eacc0b3eb5ca5e17b266cfab6e1c012a562e34","observation_id":"d7c4468a-f2dc-4c5c-a270-54763ccb3042","resolution":{"observed_at":"2026-08-11T23:24:35.807081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.811872Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.811872Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:031477d0bd585ce84d22abe14628378f202e2121d33d57819775337f55049556","observation_id":"5654d59b-c20f-4e94-873b-56c48a663d1c","resolution":{"observed_at":"2026-08-11T23:24:35.811872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.816494Z","title":"Burhanudin and Justyn R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.816494Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b1bc5eb755f44329fbbc5a1b718f2c536b757f8f0e1930ab790a0cb9718a9e69","observation_id":"62b77a90-9814-44a4-80b5-e78d78540f11","resolution":{"observed_at":"2026-08-11T23:24:35.816494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.821203Z","title":"Burns, Emilie Parent, M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.821203Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:8d8b753f662b05a845e5b70b9cdebdf205e5f25114cd6775cb757cbf5f50583b","observation_id":"6f4969d5-0678-42f1-8496-65690c3edf89","resolution":{"observed_at":"2026-08-11T23:24:35.821203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.826215Z","title":"Caldwell, Peter Tenenbaum, Joseph D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.826215Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:dc13e625b7b7d0385799a83ddc0fb0ee792d09e009e7a5568270093003b5fe83","observation_id":"6a9309db-9640-4f3e-8c7b-9000cb01e599","resolution":{"observed_at":"2026-08-11T23:24:35.826215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.831245Z","title":"Davis, Dan Scolnic, Khaled Said, Dillon Brout, Erik R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.831245Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:01a9a803883da25e04bac42635bb3ea904894aacd15117ed87b0885824e87358","observation_id":"9a92f5c3-25cb-4433-ac40-c7ad3ea6443f","resolution":{"observed_at":"2026-08-11T23:24:35.831245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.835976Z","title":"Andrews, José Sánchez-Gallego, Joel Brownstein, María Argudo-Fernández, Michael Blanton, Kevin Bundy, Amy Jones, Karen Masters, David R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.835976Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:314519b30c4057787ea78c1b413ca8133dc2a1d4863cf5a1d613b603e5d7fb09","observation_id":"b2ad72c1-daf8-45ad-ad4d-a78ac9bdd975","resolution":{"observed_at":"2026-08-11T23:24:35.835976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.840384Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.840384Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:ee3913a5677f89bd58d55723bfc82bfe34cbe39367135bd96355153cc239b40f","observation_id":"7fca441b-bad9-44fd-b064-3877909cd60e","resolution":{"observed_at":"2026-08-11T23:24:35.840384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.845005Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.845005Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e0fe201520b6a19796b557448472a85a0992475c69c6ed711c3841337b4e60fe","observation_id":"eefbe2db-0344-4458-bff5-3f67b34076f1","resolution":{"observed_at":"2026-08-11T23:24:35.845005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.849718Z","title":"De Angeli, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.849718Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:6b16ba2087862bb86219f52b8a005aaf5331d520ae0d9d8b6e8dff56ab475e40","observation_id":"388a67b0-75b6-47d0-a193-51977f37540a","resolution":{"observed_at":"2026-08-11T23:24:35.849718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.854303Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.854303Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:2b76ea88084050a8044621be54478553597c244775214a26a9e475b9169325d5","observation_id":"e9d46a34-4570-476a-b452-af05ebd7bec9","resolution":{"observed_at":"2026-08-11T23:24:35.854303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06308","last_updated":"2024-10-17T05:25:34Z","snapshot_observed_at":"2026-08-13T11:19:54.678834Z","submitted_at":"2023-06-09T23:55:37Z","title":"The Early Data Release of the Dark Energy Spectroscopic Instrument","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.06308","snapshot_observed_at":"2026-08-11T23:24:35.859228Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.859228Z"},"links":{"cited_paper":"/paper/2306.06308","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:581a3b493e8aa6bc7acb2b2aba0de2338ad38db9defcce7acdbce539b268e847","observation_id":"704b841b-94ca-401d-92b5-6dfca9e5d796","resolution":{"observed_at":"2026-08-11T23:24:35.859228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.00036","last_updated":"2016-12-13T15:55:51Z","snapshot_observed_at":"2026-07-29T15:27:21.480241Z","submitted_at":"2016-10-31T20:47:42Z","title":"The DESI Experiment Part I: Science,Targeting, and Survey Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.00036","snapshot_observed_at":"2026-08-11T23:24:35.864373Z","title":"Allen, Carlos Allende Prieto, James Annis, Stephen Bailey, Christophe Balland, Otger Ballester, Charles Baltay, Lucas Beaufore, Chris Bebek, Timothy C","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.864373Z"},"links":{"cited_paper":"/paper/1611.00036","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f6785b2c7080a27c1e409e4d72bd33bbd5301803b400b90bfff0c56e3ec8a99d","observation_id":"bcaff576-5929-4367-a577-2930243fd4bf","resolution":{"observed_at":"2026-08-11T23:24:35.864373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.870068Z","title":"Overview of the desi legacy imaging surveys","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.870068Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:3bcbc4496c51bddeb66b0884215bc6904832b2a076e75b3995ad035561d989fd","observation_id":"f6426c3a-c94f-4659-ab24-b1676eca1b81","resolution":{"observed_at":"2026-08-11T23:24:35.870068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.875031Z","title":"Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.875031Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:592d118e948748364c6d655f7175e36f09d05629a7480b3c3248a9d210fa80cf","observation_id":"60010ef3-f3f7-4e2f-a243-ef9be1ef2f9c","resolution":{"observed_at":"2026-08-11T23:24:35.875031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.879857Z","title":"Schlegel, Dustin Lang, Robert Blum, Kaylan Burleigh, Xiaohui Fan, Joseph R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.879857Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:85769f46cfb601354bd7ec21def2698b2df67011ae261adac0c8ebf8a4fd3671","observation_id":"1c268900-4e52-4151-a430-d6c909d51cb9","resolution":{"observed_at":"2026-08-11T23:24:35.879857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.884936Z","title":"Willett, and Joni Dambre","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.884936Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:51972e3f20e5e5aa2b3ff1388e4412a0af77b48703c170d81e84d3c937979ec2","observation_id":"70042103-3b99-4081-a95d-050b12762547","resolution":{"observed_at":"2026-08-11T23:24:35.884936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.889719Z","title":"Dobryakov, K","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.889719Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:fd34c9b06e9ef46d7f8aaa43cf3ad081451e48cf1860b14072d215415ed81178","observation_id":"e1d560f0-3fa4-4562-98ee-7a40e228b584","resolution":{"observed_at":"2026-08-11T23:24:35.889719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.894434Z","title":"Domínguez Sánchez, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.894434Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:fb776fed8c0c801f270863a8b77b9aae3ec1a8bf234e7edbfeb99499babbe44b","observation_id":"4476edc8-44f7-4552-a233-689ee4ca0fa8","resolution":{"observed_at":"2026-08-11T23:24:35.894434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.899088Z","title":"Dunlop, Roberto G","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.899088Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:5e5a59febde33ec3097bbc4501ce77dd85df1b881d783fd1651fce549a952029","observation_id":"796de7ff-945a-42b0-adb7-48f7a8c82833","resolution":{"observed_at":"2026-08-11T23:24:35.899088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.903890Z","title":"Eisenstein, David H","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.903890Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a1d95fe443e105405cf10bdf614d736553c2150ba88f127b5fd5510ee44c3505","observation_id":"2bd80929-36cc-4d72-89e4-4e5773364051","resolution":{"observed_at":"2026-08-11T23:24:35.903890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02465","last_updated":"2025-12-17T15:56:28Z","snapshot_observed_at":"2026-08-03T19:26:59.807016Z","submitted_at":"2023-06-04T20:44:21Z","title":"Overview of the JWST Advanced Deep Extragalactic Survey (JADES)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02465","snapshot_observed_at":"2026-08-11T23:24:35.908839Z","title":"Eisenstein, Chris Willott, Stacey Alberts, Santiago Arribas, Nina Bonaventura, Andrew J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.908839Z"},"links":{"cited_paper":"/paper/2306.02465","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:3d627716898ca5a5b189913aeb85ec80e1324227ddcd93d80e8f6278fc9beaa1","observation_id":"b8566034-ae51-441f-b884-05639307782a","resolution":{"observed_at":"2026-08-11T23:24:35.908839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.914009Z","title":"Evans, Francis A","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.914009Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:7f3e01f4bc9b97ce28c01ecfb3ed813b30b7cebb410b5f7ee248e288380d1bb7","observation_id":"a6329a76-d45a-4845-a4cf-b6b53dc06e27","resolution":{"observed_at":"2026-08-11T23:24:35.914009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.918788Z","title":"Finkelstein, Micaela B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.918788Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b54383804d0a406b95cc59439725f0bbdf7617e53684bf9da81a3c40c2444067","observation_id":"686d890c-4c8e-44f7-bef3-49616cbc6029","resolution":{"observed_at":"2026-08-11T23:24:35.918788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.923641Z","title":"Foley, Daniel Scolnic, Armin Rest, S","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.923641Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f408a861422fbe787638fd76361ca436d2b0c4e4763e823fc5517a37f7cd47b4","observation_id":"f310bb3a-fb5c-42eb-8eb7-dd1bc0040fde","resolution":{"observed_at":"2026-08-11T23:24:35.923641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12095","last_updated":"2024-06-20T18:32:23Z","snapshot_observed_at":"2026-08-13T04:15:28.466591Z","submitted_at":"2024-02-19T12:23:39Z","title":"Major TOM: Expandable Datasets for Earth Observation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12095","snapshot_observed_at":"2026-08-11T23:24:35.928556Z","title":"Francis and M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.928556Z"},"links":{"cited_paper":"/paper/2402.12095","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:cd119ddf33b8efd6be88ab4d7332a6270af45b4fd76b8052b9f15504e065d4ff","observation_id":"b796aeda-6564-427f-a830-50c450f365ac","resolution":{"observed_at":"2026-08-11T23:24:35.928556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.933612Z","title":"Freedman, Barry F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.933612Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:9551b97669568e92ff0b3800f0b2bb7f0f2ed0b1049fd3c52a8e1ec84a66e388","observation_id":"297ab5d1-8b7d-43ff-a2e7-87bb368f8371","resolution":{"observed_at":"2026-08-11T23:24:35.933612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.938418Z","title":"Fremling, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.938418Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:391434ef46e763cd8a76a8dab799b83feaae0e95e6457cd57f2e5d90b8880556","observation_id":"98c9bafd-a94b-4d16-8d7a-ad00e703fed8","resolution":{"observed_at":"2026-08-11T23:24:35.938418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.943240Z","title":"Drimmel, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.943240Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e4bf4f69a0542d7b45441a886321630ecdbeb34e2ceb6bb292bf22ef86bd9322","observation_id":"82c9c399-de1e-4e1a-839b-0ab2d0c670ab","resolution":{"observed_at":"2026-08-11T23:24:35.943240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.948659Z","title":"Recio-Blanco, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.948659Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:60ed1fd10505b545cfc53426fcc7e571943bdaffd61f6d9b558a2fc8736a7e8d","observation_id":"f7da9e23-5b31-4c4a-bf83-d67d13d10af9","resolution":{"observed_at":"2026-08-11T23:24:35.948659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.953748Z","title":"Vallenari, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.953748Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:4dd0c0187b5b4956a0ef2aa22889e729a21429cb0ef6622aaa2f28e886b60e86","observation_id":"391c6b13-11b3-4196-9b68-e3a2c456dea9","resolution":{"observed_at":"2026-08-11T23:24:35.953748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00027","last_updated":"2020-12-31T19:00:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-12-31T19:00:10Z","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00027","snapshot_observed_at":"2026-08-11T23:24:35.958578Z","title":"The pile: An 800gb dataset of diverse text for language modeling","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.958578Z"},"links":{"cited_paper":"/paper/2101.00027","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a4dc89b85ae603bfaaf950d15dc1339e6e5becdd37ef54d06d52fe2c7aa26d9d","observation_id":"c213317f-5e05-4d80-851d-11ab19403037","resolution":{"observed_at":"2026-08-11T23:24:35.958578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.963856Z","title":"García Pérez, Carlos Allende Prieto, Jon A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.963856Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:8e215b86f4d79559a9ab75edbff69d73626e7831734b6a66beaef9b5e1a6e99f","observation_id":"8b265a9e-7f79-4478-9d67-a3145253f610","resolution":{"observed_at":"2026-08-11T23:24:35.963856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03885","last_updated":"2024-10-10T15:37:45Z","snapshot_observed_at":"2026-08-15T14:30:28.053362Z","submitted_at":"2024-02-06T10:48:46Z","title":"MOMENT: A Family of Open Time-series Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03885","snapshot_observed_at":"2026-08-11T23:24:35.969151Z","title":"Goswami, K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.969151Z"},"links":{"cited_paper":"/paper/2402.03885","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e9c359b5544e236605d6ff3268f209b9fe4a92bc4fede306e7b7a18d705048be","observation_id":"5ccc6bac-dbfe-4466-a61d-180fd4545253","resolution":{"observed_at":"2026-08-11T23:24:35.969151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.979177Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.979177Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f232c475e8d984a93d59c380b51096ff441029ee928a979adee464d5ca29fef3","observation_id":"efe7bf4f-84b0-4d98-b4ee-c240f2a6a4f1","resolution":{"observed_at":"2026-08-11T23:24:35.979177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.983733Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.983733Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:25928d9a001ecfb38e277f595823c49f3ed6f3d854d9de2a76decdbc67e6229d","observation_id":"30350640-f257-45d3-8695-033e958159ab","resolution":{"observed_at":"2026-08-11T23:24:35.983733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.988943Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.988943Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a76e429bed7fec6197774dc51ebb7fe2880af1cc3e2939f51a99877c9ccc98a4","observation_id":"fb39f8c3-4ea4-4923-b433-1c8bd05f6a2f","resolution":{"observed_at":"2026-08-11T23:24:35.988943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.993531Z","title":"Hebbar and Craig O","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.993531Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:53465eec668f28c1841fe21bb4c8f08e8a10eeaf68c7d5f1f66fd2308b285d2c","observation_id":"bf2e7935-6024-46ad-b45c-61a515d9821d","resolution":{"observed_at":"2026-08-11T23:24:35.993531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:35.998343Z","title":"Kirshner, Tom Matheson, Maryam Modjaz, Armin Rest, W","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:35.998343Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a7a316a0f1a9d6a1189297a96a5d664ae3cbcde6590979388c122c8a1efd05da","observation_id":"dc90a104-7f6e-44dc-8d76-bd48b8bfd512","resolution":{"observed_at":"2026-08-11T23:24:35.998343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.003165Z","title":"Kirshner, Armin Rest, Claire E","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.003165Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a52eb96461ec725b65ea1a24dc9b1ad6b40c588894583629fa21e405eaa4e221","observation_id":"26228948-9480-49e0-a548-9c681a86775e","resolution":{"observed_at":"2026-08-11T23:24:36.003165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.007980Z","title":"Friedman, Stephane Blondin, Peter Challis, Perry Berlind, Mike Calkins, Gil Esquerdo, Thomas Matheson, Maryam Modjaz, Armin Rest, and Robert P","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.007980Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:9da20b6faa6d576b68c8ac7a6507037a8a9c9ef0fc591e8c7aebdab1699ba303","observation_id":"1feafd73-1687-43a8-88bf-bbf05bb7af7f","resolution":{"observed_at":"2026-08-11T23:24:36.007980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.012494Z","title":"Hložek, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.012494Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:7337ec4e98181cdf20341e0c139ecc42e5294074baff865369e30bfad6462271","observation_id":"9c273eba-fe49-45a5-a1ee-3a6838724bb5","resolution":{"observed_at":"2026-08-11T23:24:36.012494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.017317Z","title":"Quick Look","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.017317Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:864b6e1c8e1eb6b1ede045a343b36579a08e21528f6dea5a1135d0cc9105dd14","observation_id":"f976263f-77dc-4697-a94d-e97e5fcaa653","resolution":{"observed_at":"2026-08-11T23:24:36.017317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.021976Z","title":"Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.021976Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:6ba5e34b18f5ebcb17a536690a16822581ebde3f371e8f3e50d23df701837181","observation_id":"4f1f327e-cf30-4c62-a8e2-a012e0066222","resolution":{"observed_at":"2026-08-11T23:24:36.021976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.027128Z","title":"Huang, Andrew Vanderburg, Andras Pál, Lizhou Sha, Liang Yu, Willie Fong, Michael Fausnaugh, A vi Shporer, Natalia Guerrero, Roland Vanderspek, and George Ricker","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.027128Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a7cec39c53ef033c3cce0736a1ca1d7a86430e484944aeaa0f5244001f6a0e0e","observation_id":"0e074154-1443-450f-9f44-5621308d6f0b","resolution":{"observed_at":"2026-08-11T23:24:36.027128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.031910Z","title":"Densely connected convolutional networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.031910Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:3e8ce9f5bc74bfe758cfbfd35b54f1c1c9646cab3c00fbeb77bf652bc9742209","observation_id":"6b1930be-595f-4b95-bdf1-e30709c45ce8","resolution":{"observed_at":"2026-08-11T23:24:36.031910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.036629Z","title":"Huang, C","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.036629Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:2fcb768ccb1b9f19dbde4348fd217bb960e72bb5c5ed146dfb44303aaad70c70","observation_id":"502b01de-47fa-41d7-828c-596925056b5a","resolution":{"observed_at":"2026-08-11T23:24:36.036629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.041750Z","title":"Huertas-Company, K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.041750Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:7fddc26dd34a0074aa74c1803e0ebd45234972861be5f6bff9338cd2681fbf92","observation_id":"35e6df85-6e0d-41bf-896a-10d066ec64cd","resolution":{"observed_at":"2026-08-11T23:24:36.041750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.046559Z","title":"Huertas-Company and F","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.046559Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:077a5528eb608ab635dcd5b729ca44f122bb1d7c9256de25b1ed86734faf3322","observation_id":"e63dae7f-3f0a-402e-88e7-5e94c60d86f8","resolution":{"observed_at":"2026-08-11T23:24:36.046559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.051560Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.051560Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:569a784d7020481fc3be2db8c2f1406a611d51f8261743562e74371b31c6d5cf","observation_id":"c234850c-6e2c-4e3d-9212-6f38696ccf68","resolution":{"observed_at":"2026-08-11T23:24:36.051560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.056625Z","title":"Jenkins, Joseph D","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.056625Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:f38305d2d09d0712afd4943d7d14962deea6a8a71cfa613bb50f8a9e876cf84d","observation_id":"7f253ac5-edd0-4948-9871-92c1418e1f90","resolution":{"observed_at":"2026-08-11T23:24:36.056625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.061709Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.061709Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:075287b1d3ae9b5b77e489f62ac798ebdde32c05124f0f68852c5b252c465810","observation_id":"4a5d9f4c-c88d-437a-bfce-97817a8143ef","resolution":{"observed_at":"2026-08-11T23:24:36.061709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.066967Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.066967Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:ef945bd565de85c465f738ce232a8b64ca29931fe68ca424524528316fee7242","observation_id":"728e091b-7fe7-409a-b41b-973cc81fc970","resolution":{"observed_at":"2026-08-11T23:24:36.066967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.071782Z","title":"Frieman, Alexandre Glazov, Santiago González-Gaitán, Renée Hlozek, Saurabh Jha, Stephen Kuhlmann, Martin Kunz, Hubert Lampeitl, Ashish Mahabal, James Newling, Robert C","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.071782Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:395b49c53be87d161d418219bdf024d9d9a5a6b66332904c6c269f9262b9b2df","observation_id":"84cfb935-58db-4d57-8159-64ee73489868","resolution":{"observed_at":"2026-08-11T23:24:36.071782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T23:24:36.076436Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.076436Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:21be930ca50816bf54768c30be4a73ec384988217b693dbf606f15c9d5f2d180","observation_id":"6423d1aa-87d4-41e6-bf9b-28982a3c60d9","resolution":{"observed_at":"2026-08-11T23:24:36.076436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.081434Z","title":"Koch, William J","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.081434Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e37258d31b74d7ba4142da9b8518c9abdfd021fde47a71435fb482352bb9ee1a","observation_id":"1a082120-db8e-4910-b0e7-90fa107a8f2a","resolution":{"observed_at":"2026-08-11T23:24:36.081434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.086604Z","title":"Burns, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.086604Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:5ab2a108dae8582975c21a85c6728256b62897125c9627bc0e14abf472b5d161","observation_id":"c169fee2-b467-4240-8d03-1dd7b9df82ad","resolution":{"observed_at":"2026-08-11T23:24:36.086604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.091422Z","title":"Quick-look Pipeline Lightcurves for 9.1 Million Stars Observed over the First Year of 34 the TESS Extended Mission","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.091422Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:1c484edc8befda92cb319510a1974f0529ba556862431fc2b43cf16edcd0fa3b","observation_id":"578a7bc6-0dc8-4b71-811f-cc6dfbc941e3","resolution":{"observed_at":"2026-08-11T23:24:36.091422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.096239Z","title":"Quick-look Pipeline Light Curves for 5.7 Million Stars Observed Over the Second Year of TESS’ First Extended Mission","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.096239Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:902900512d13927df0a470ea777bd474ac94f9ce83a3739bfa090961ee1adecc","observation_id":"3287985d-e539-4c71-bf5c-0d3356011181","resolution":{"observed_at":"2026-08-11T23:24:36.096239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.100912Z","title":"Law, Brian Cherinka, Renbin Yan, Brett H","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.100912Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:7729c86212391b4bc5fd7f0243f028198e2714bd97b60933f0447db8d0169cae","observation_id":"77eed771-9c5a-4728-92a0-fc2c0531c490","resolution":{"observed_at":"2026-08-11T23:24:36.100912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.106638Z","title":"Lee, Ralf Gommers, Filip Waselewski, Kai Wohlfahrt, and Aaron O’Leary","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.106638Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:846cb543b1d639a33a05e5c83f121fa42e36dd240fb192145085aace91f93ef1","observation_id":"f0e8de52-ae68-4a1d-a963-c613a9951144","resolution":{"observed_at":"2026-08-11T23:24:36.106638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.111897Z","title":"Leoni, E","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.111897Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:6e095354b8d7ebf2f49729e4375932ad29d47aa9af79c8e0d89b2271ad959937","observation_id":"2671c1e7-45ac-40aa-ae32-0a6e16bc962e","resolution":{"observed_at":"2026-08-11T23:24:36.111897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.116570Z","title":"Galaxy10 decals dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.116570Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b5e8c1a21975af2ecf9ff83df0f552e8e32626adae75f755da8f63b8bad50e66","observation_id":"9b6e189c-f1bb-4711-a35f-ed1438c997fa","resolution":{"observed_at":"2026-08-11T23:24:36.116570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.121372Z","title":"Deep learning of multi-element abundances from high- resolution spectroscopic data","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.121372Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:a60897de5b0110e945572fc21d6b5108f02dbb7a0e042661d4d647b53cd845d5","observation_id":"3244e109-74a9-4cd7-b6c0-13a79009557e","resolution":{"observed_at":"2026-08-11T23:24:36.121372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.126239Z","title":"Leung and Jo Bovy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.126239Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:12c26f9dda9872b037d0fb26da9b2695eb6210c4629eb76522708688e178d226","observation_id":"26c2b3af-ff74-4e5d-83f1-b6446498141a","resolution":{"observed_at":"2026-08-11T23:24:36.126239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.131051Z","title":"Learnable fourier features for multi-dimensional spatial positional encoding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.131051Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:559ea1df7f0804cdc1130af746fa3e2a99fa1775fa0fc46170056a0a08dec5e2","observation_id":"31c155b8-83e6-4931-a150-8cd42c9e16a0","resolution":{"observed_at":"2026-08-11T23:24:36.131051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.136250Z","title":"Outlier Detection in the DESI Bright Galaxy Survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.136250Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:b584526ee4c36fab3bc78849b83b10ffd00486e0d1ea4353f2efc9596f36fd76","observation_id":"c62ebb10-17ae-4aeb-a7fa-34f421aa6089","resolution":{"observed_at":"2026-08-11T23:24:36.136250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:36.141069Z","title":"Nichol, M","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.141069Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:103224cedfb820feebca51e552196f91400b46e710bf72c1eb4bb4fa2d072e50","observation_id":"f55ec33b-692e-4395-9def-dad655b75c8f","resolution":{"observed_at":"2026-08-11T23:24:36.141069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:38.016302Z","title":"Galaxy zoo 1: data release of morphological classifications for nearly 900 000 galaxies","venue":null,"work_id":"3a11e9c5-4f20-4fee-99a9-f3babdb9231f","year":2011},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.145969Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:0e9ff23fe14d44c05d4611701b75fc29c53769b7dd0dedb6be8b869dc2763a66","observation_id":"e369d3b3-ad77-4dcb-8240-86076b1865d9","resolution":{"observed_at":"2026-08-11T23:24:38.021898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:38.000982Z","title":"Lintott, Kevin Schawinski, Anže Slosar, Kate Land, Steven Bamford, Daniel Thomas, M","venue":null,"work_id":"427268f1-b6db-4a67-9434-45d6b57bf59e","year":2008},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.150638Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:e0ac95fd2d028b093bf3e29f96d6bd8e4fa2f65bc937bef0ee9a3e5e8d425f6f","observation_id":"8c029745-f7f3-41d6-97a9-d1e50519c3f5","resolution":{"observed_at":"2026-08-11T23:24:38.006118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:37.985227Z","title":"Allam, Tarek, Rahul Biswas, Johnny Holland, Ofer Lahav, Robert Schuhmann, Christian Setzer, and Max Winter","venue":null,"work_id":"12e48ebe-c454-4b8f-bff7-19735f7f5578","year":2021},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.155370Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:219fa67438ce7129b6a08ba23d18f627afb085e3fb01a6a9399bcfe9e529b90e","observation_id":"b76a1701-e829-4fce-ad67-55084b74eaf4","resolution":{"observed_at":"2026-08-11T23:24:37.990466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:37.968389Z","title":"McEwen, Hiranya V","venue":null,"work_id":"733be04e-d9a3-4cc6-ae82-1089468559bc","year":2016},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.160352Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:41246c1130788dc0bbab56c24cd60634bba0646bdf9b49113bf202fee1ae9f94","observation_id":"921ac086-36cf-46b2-a482-04411bcb3339","resolution":{"observed_at":"2026-08-11T23:24:37.973457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:24:37.951206Z","title":"Majewski, Ricardo P","venue":null,"work_id":"1ae9f7d2-3e49-493a-9d5c-45306d6596b2","year":2017},"citing_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-11T23:24:36.165071Z"},"links":{"citing_paper":"/paper/2412.02527"},"observation_digest":"sha256:898d4c7e27916cf59a688592404467fcb614e1f395927e05bbe64540ff18bc3f","observation_id":"a7fb221b-d1c6-4f98-a628-a07f816a7e67","resolution":{"observed_at":"2026-08-11T23:24:37.956388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","latest_version":1,"primary_category":"astro-ph.IM","snapshot_observed_at":"2026-08-14T03:01:37.636092Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":94,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":167},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 100 of 167 outbound references and 7 inbound Pith citation observations for arXiv:2412.02527."}