{"as_of":"2026-08-19T18:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f5f2c70b30feb6254f28334a026d532a603b020c6f301cc4797e097655fb3b0","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T21:30:55.070941Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2511.15072/citation-record","integrity":"/paper/2511.15072/integrity","json":"/paper/2511.15072/citation-record.json","paper":"/paper/2511.15072"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:30:49.018835Z","title":"In our case, PCA is applied to each of the 192 simulated sky patches, with each 643 data cube reshaped into a collection of one-dimensional spectra along the fre- 7 quency axis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.018835Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ff079ed82895dd1ac80b353138f225f19d15ed8973f160eabe489bf111151649","observation_id":"0fbaf037-79cb-47e6-aeeb-825eb41d803d","resolution":{"observed_at":"2026-08-03T21:30:49.018835Z","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":"2026-08-03T21:30:49.099537Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.099537Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:f1ba27f8ccdef4a49e0ab1c1a413a3dfccafa1d61cf6137cb8745b40d9e38b71","observation_id":"13cbe686-d57a-4a1f-8bc2-9cfc64a34df1","resolution":{"observed_at":"2026-08-03T21:30:49.099537Z","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":"2026-08-03T21:30:49.165634Z","title":"We preprocess the input data cube inde- pendently using PCA and frequency differencing, yield- ing two separate residual maps","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.165634Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:d94d8313bc88ed6c0ea2a0a52768398d4d255bba644a15bb73aa7eab3f207323","observation_id":"0a3eba0b-13dd-43c7-8719-0a35afab449f","resolution":{"observed_at":"2026-08-03T21:30:49.165634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06518","last_updated":"2024-03-12T09:41:52Z","snapshot_observed_at":"2026-08-16T14:53:29.727652Z","submitted_at":"2023-10-10T11:00:49Z","title":"21-cm foreground removal using AI and frequency-difference technique","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06518","snapshot_observed_at":"2026-08-03T21:30:49.274019Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.274019Z"},"links":{"cited_paper":"/paper/2310.06518","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:be2b11ebe0560ee2fd8d7046f3940514ae17bd19852523ddd500be2e66c67444","observation_id":"d9614be6-8582-4d33-ab78-ae8a1aa83d2b","resolution":{"observed_at":"2026-08-03T21:30:49.274019Z","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":"2026-08-03T21:30:49.358689Z","title":"Bharadwaj, B","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.358689Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:70b459e8cc0cc4f9987087cb19b54d993ce96ba37023d0e27adc200b8dc63aea","observation_id":"888d754a-a654-444e-9699-c1ac4172fcb5","resolution":{"observed_at":"2026-08-03T21:30:49.358689Z","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":"2026-08-03T21:30:49.428143Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.428143Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:571da939f889f480cb736d2dbff7c7b7920934d3719ab03fb55c8049e9bdcbe2","observation_id":"dc8327ea-21db-401f-98d7-7b77b1f10a69","resolution":{"observed_at":"2026-08-03T21:30:49.428143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0902.3091","last_updated":"2009-02-18T10:33:08Z","snapshot_observed_at":"2026-08-15T05:54:45.782554Z","submitted_at":"2009-02-18T10:33:08Z","title":"21 cm Intensity Mapping","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0902.3091","snapshot_observed_at":"2026-08-03T21:30:49.484267Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.484267Z"},"links":{"cited_paper":"/paper/0902.3091","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:c443fec2a4e1f21e6a94428dd39f4c1684063c86651246d843e71dae28bd2599","observation_id":"32f6f075-945f-40d4-ad58-b8bd6c0920dd","resolution":{"observed_at":"2026-08-03T21:30:49.484267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0808.2323","last_updated":"2009-07-24T05:28:34Z","snapshot_observed_at":"2026-08-15T10:08:47.191295Z","submitted_at":"2008-08-18T00:12:53Z","title":"The 21cm Power Spectrum After Reionization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0808.2323","snapshot_observed_at":"2026-08-03T21:30:49.561061Z","title":null,"venue":null,"work_id":null,"year":1926},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.561061Z"},"links":{"cited_paper":"/paper/0808.2323","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:343ca5dfaf5e117444715a0a3685736f2fe6291a99a59f328a7f01a96dcd104e","observation_id":"c5f6beab-3c1f-4724-8ef8-9876e2795a79","resolution":{"observed_at":"2026-08-03T21:30:49.561061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0709.3672","last_updated":"2008-01-27T22:20:59Z","snapshot_observed_at":"2026-08-15T10:31:43.076762Z","submitted_at":"2007-09-23T23:20:08Z","title":"Baryon Acoustic Oscillation Intensity Mapping as a Test of Dark Energy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0709.3672","snapshot_observed_at":"2026-08-03T21:30:49.644473Z","title":"Chang, U.-L","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.644473Z"},"links":{"cited_paper":"/paper/0709.3672","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:f37345cdcdc01e15842e8ed8c8245a89e86f14355fc498c6fd193db81af84dc4","observation_id":"7e5b539d-7b41-4c32-812a-e45d3d091884","resolution":{"observed_at":"2026-08-03T21:30:49.644473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0910.5007","last_updated":"2010-07-01T17:22:55Z","snapshot_observed_at":"2026-08-18T21:37:02.516960Z","submitted_at":"2009-10-26T23:56:36Z","title":"A ground-based 21cm Baryon acoustic oscillation survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0910.5007","snapshot_observed_at":"2026-08-03T21:30:49.709995Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.709995Z"},"links":{"cited_paper":"/paper/0910.5007","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:7429bed1107d438a1aeb3743036cf74bb7dd75f91c4ab8257bcfaa95c1999312","observation_id":"2bca87de-e3a1-4790-84c9-21c099993303","resolution":{"observed_at":"2026-08-03T21:30:49.709995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.07681","last_updated":"2023-03-23T07:03:27Z","snapshot_observed_at":"2026-08-16T16:08:30.078880Z","submitted_at":"2022-12-15T09:27:37Z","title":"Prospects for measuring dark energy with 21 cm intensity mapping experiments: A joint survey strategy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.07681","snapshot_observed_at":"2026-08-03T21:30:49.881523Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.881523Z"},"links":{"cited_paper":"/paper/2212.07681","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:11fd8440533d4ee9c790ae6c1b9cc79ce729acf3dfae79274ac2fee4cb5e98a6","observation_id":"68a9a3f8-524b-45b5-8c63-68100f9b6893","resolution":{"observed_at":"2026-08-03T21:30:49.881523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.01242","last_updated":"2022-02-02T19:00:11Z","snapshot_observed_at":"2026-08-16T17:24:02.602743Z","submitted_at":"2022-02-02T19:00:11Z","title":"Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01242","snapshot_observed_at":"2026-08-03T21:30:49.966047Z","title":"Amiri, K","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:49.966047Z"},"links":{"cited_paper":"/paper/2202.01242","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:24a1fed4e7131ddf804381b319921092c35f97e4da978f313faf9d038886f814","observation_id":"ae738599-e792-46c8-b718-e66fb81ffc25","resolution":{"observed_at":"2026-08-03T21:30:49.966047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07869","last_updated":"2022-05-24T02:44:45Z","snapshot_observed_at":"2026-08-16T17:27:14.100390Z","submitted_at":"2022-01-19T21:35:07Z","title":"An Overview of CHIME, the Canadian Hydrogen Intensity Mapping Experiment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.07869","snapshot_observed_at":"2026-08-03T21:30:50.035973Z","title":"Amiri, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.035973Z"},"links":{"cited_paper":"/paper/2201.07869","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:9afc3d74705cc4c4f4fd673d8febee85a438e087b129f49770e0d1a320c8d4b5","observation_id":"6acf599e-4ebb-4389-87f4-e4dda1b5f50a","resolution":{"observed_at":"2026-08-03T21:30:50.035973Z","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":"2026-08-03T21:30:50.122257Z","title":"Chen, Radio detection of dark energy—the Tianlai project, Scientia Sinica Physica, Mechanica & As- tronomica41, 1358 (2011)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.122257Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ea9f22352c04d0e2554b595b59ea5f0555d0901a4291605ac0f52b9f42f9f607","observation_id":"95e63c6c-929a-48c5-81a2-2d6721794e41","resolution":{"observed_at":"2026-08-03T21:30:50.122257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1410.7794","last_updated":"2014-10-28T20:37:28Z","snapshot_observed_at":"2026-08-14T23:13:54.537884Z","submitted_at":"2014-10-28T20:37:28Z","title":"Forecasts on the Dark Energy and Primordial Non-Gaussianity Observations with the Tianlai Cylinder Array","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1410.7794","snapshot_observed_at":"2026-08-03T21:30:50.204446Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.204446Z"},"links":{"cited_paper":"/paper/1410.7794","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:6fa0e6aa715406929c6c2d4607cda01a1de44f274ce7162d6a479f8f1cc37add","observation_id":"675fcb9f-357d-4028-9389-63bdf0af883d","resolution":{"observed_at":"2026-08-03T21:30:50.204446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.05946","last_updated":"2021-06-27T08:10:39Z","snapshot_observed_at":"2026-08-16T19:06:32.700113Z","submitted_at":"2020-11-11T18:04:24Z","title":"The Tianlai Dish Pathfinder Array: design, operation and performance of a prototype transit radio interferometer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.05946","snapshot_observed_at":"2026-08-03T21:30:50.268264Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.268264Z"},"links":{"cited_paper":"/paper/2011.05946","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:c36f431579a26453d836244b0391e5c42604b8fcd67490dfd4ee85dd5f5b4f9f","observation_id":"389e2b8d-d928-4b2a-8662-aa3f7cb7c823","resolution":{"observed_at":"2026-08-03T21:30:50.268264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.06086","last_updated":"2022-05-12T13:44:02Z","snapshot_observed_at":"2026-08-17T21:20:54.515880Z","submitted_at":"2022-05-12T13:44:02Z","title":"The Tianlai dish array low-z surveys forecasts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.06086","snapshot_observed_at":"2026-08-03T21:30:50.350521Z","title":"Perdereau, R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.350521Z"},"links":{"cited_paper":"/paper/2205.06086","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:c09b73fb5daf68525612916cac1cc3636d054f67061dc565d3278c68bdb55dd3","observation_id":"44a78e74-6bf0-40ca-a874-30227699d5ac","resolution":{"observed_at":"2026-08-03T21:30:50.350521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1209.0343","last_updated":"2012-09-03T13:23:08Z","snapshot_observed_at":"2026-08-15T00:48:00.736946Z","submitted_at":"2012-09-03T13:23:08Z","title":"HI intensity mapping : a single dish approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1209.0343","snapshot_observed_at":"2026-08-03T21:30:50.408785Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.408785Z"},"links":{"cited_paper":"/paper/1209.0343","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:b7a2345ad7b8a15c7a94fddd308402db19bcdee1817c12c20ff1c9f97f8f38de","observation_id":"6e4c2349-4500-4c5c-b829-0f5bae92f968","resolution":{"observed_at":"2026-08-03T21:30:50.408785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01633","last_updated":"2021-10-12T14:27:25Z","snapshot_observed_at":"2026-08-16T18:12:28.763375Z","submitted_at":"2021-07-04T14:06:48Z","title":"The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01633","snapshot_observed_at":"2026-08-03T21:30:50.485367Z","title":"Abdalla, E","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.485367Z"},"links":{"cited_paper":"/paper/2107.01633","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:e5460afc888799ca86c14f29911aefaf4ac1b9fcdffe6494ce6df4979d417a84","observation_id":"127df30f-d2ec-4bac-9cef-8b0b7d4487d1","resolution":{"observed_at":"2026-08-03T21:30:50.485367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.06099","last_updated":"2017-09-18T18:00:22Z","snapshot_observed_at":"2026-08-15T01:11:25.450557Z","submitted_at":"2017-09-18T18:00:22Z","title":"MeerKLASS: MeerKAT Large Area Synoptic Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.06099","snapshot_observed_at":"2026-08-03T21:30:50.552492Z","title":"Santos, P","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.552492Z"},"links":{"cited_paper":"/paper/1709.06099","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:6e0fd8e45896eb2dc2b54fa8091fe1c2c9dec53d66c3aa4561bf2673ee034207","observation_id":"e8c3571f-fe0a-4442-8a88-2eb0d8a78751","resolution":{"observed_at":"2026-08-03T21:30:50.552492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13789","last_updated":"2021-05-13T19:34:04Z","snapshot_observed_at":"2026-08-16T19:02:49.202456Z","submitted_at":"2020-11-27T15:43:45Z","title":"HI intensity mapping with MeerKAT: Calibration pipeline for multi-dish autocorrelation observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13789","snapshot_observed_at":"2026-08-03T21:30:50.638702Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.638702Z"},"links":{"cited_paper":"/paper/2011.13789","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:70fbe7b81dc75a1742b2e75fc42eaabbc8dfdd250ea06bde5e27ca1725e99ecc","observation_id":"76836f2b-e6b8-4413-8014-d46bb9020395","resolution":{"observed_at":"2026-08-03T21:30:50.638702Z","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":"2026-08-03T21:30:50.697317Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.697317Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:26f746cf28574dff87bedffc22a3fa217068077714809e791193625299dd9e6f","observation_id":"d051e29e-321b-4252-ad37-700b3c8967f0","resolution":{"observed_at":"2026-08-03T21:30:50.697317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1501.03989","last_updated":"2015-01-16T14:33:23Z","snapshot_observed_at":"2026-08-14T23:03:49.453138Z","submitted_at":"2015-01-16T14:33:23Z","title":"Cosmology with a SKA HI intensity mapping survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1501.03989","snapshot_observed_at":"2026-08-03T21:30:50.754176Z","title":"Santos, P","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.754176Z"},"links":{"cited_paper":"/paper/1501.03989","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:1bf595a96eebc89d700a6a6781bb9f95527967712c4e3a8a7f1bca3521511d42","observation_id":"622bd995-9c4c-4ec5-a0dc-34ae75d5232d","resolution":{"observed_at":"2026-08-03T21:30:50.754176Z","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":"2026-08-03T21:30:50.810140Z","title":"Chang, U.-L","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.810140Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:e9c660a2fd2238511c2a2831d95f1bd7e7ba29abbe282e2f9e8e319baab5d783","observation_id":"4d08ce51-87f0-4a48-a3c6-cdee61cf8705","resolution":{"observed_at":"2026-08-03T21:30:50.810140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1208.0331","last_updated":"2013-01-11T21:21:13Z","snapshot_observed_at":"2026-08-17T16:45:48.474624Z","submitted_at":"2012-08-01T19:59:27Z","title":"Measurement of 21 cm brightness fluctuations at z ~ 0.8 in cross-correlation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1208.0331","snapshot_observed_at":"2026-08-03T21:30:50.863943Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.863943Z"},"links":{"cited_paper":"/paper/1208.0331","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:53adc7c15ccf0a38babfcc026531248184982c6ad7f743aeea47801af2380413","observation_id":"31991849-3961-4ab3-a560-2c327fd92add","resolution":{"observed_at":"2026-08-03T21:30:50.863943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04946","last_updated":"2021-12-09T13:37:42Z","snapshot_observed_at":"2026-08-16T18:46:43.557785Z","submitted_at":"2021-02-09T16:58:34Z","title":"HI constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04946","snapshot_observed_at":"2026-08-03T21:30:50.944409Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:50.944409Z"},"links":{"cited_paper":"/paper/2102.04946","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:d95a31444bdc40153978f4940b6c3a9c2f5cdd991d09d6ca23b8b2b643b11da9","observation_id":"a32c4bd2-a545-4a66-b9d7-75bae2bd8878","resolution":{"observed_at":"2026-08-03T21:30:50.944409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.00424","last_updated":"2017-10-09T22:22:15Z","snapshot_observed_at":"2026-08-18T16:20:43.709519Z","submitted_at":"2017-10-01T21:58:25Z","title":"Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.00424","snapshot_observed_at":"2026-08-03T21:30:51.035514Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.035514Z"},"links":{"cited_paper":"/paper/1710.00424","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:06fc501d3c4ffe3f60864cebe46cc24d70fc717b79b40c217e20da5ea386272f","observation_id":"d05a32ba-3707-4b7d-97de-7dcbccce25c8","resolution":{"observed_at":"2026-08-03T21:30:51.035514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01579","last_updated":"2022-10-25T08:24:42Z","snapshot_observed_at":"2026-08-19T13:17:25.430846Z","submitted_at":"2022-06-03T14:03:31Z","title":"HI intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01579","snapshot_observed_at":"2026-08-03T21:30:51.068452Z","title":"Cunnington, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.068452Z"},"links":{"cited_paper":"/paper/2206.01579","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:c8722fdade26b14cf235b0b9766342876290be92b1219c4e0532a111561ee8fe","observation_id":"8b9cd980-0784-4eb4-b3aa-718621254907","resolution":{"observed_at":"2026-08-03T21:30:51.068452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17564","last_updated":"2025-06-12T15:24:57Z","snapshot_observed_at":"2026-08-18T22:43:48.144168Z","submitted_at":"2025-01-29T10:57:02Z","title":"HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17564","snapshot_observed_at":"2026-08-03T21:30:51.139330Z","title":"Mazumder, L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.139330Z"},"links":{"cited_paper":"/paper/2501.17564","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:bdb1b7db4f5f97c3d70703b7e66b7e9a49f120285efac6c243f8c259b0885a82","observation_id":"f2ab15d6-3c77-462b-85f1-497005a9449e","resolution":{"observed_at":"2026-08-03T21:30:51.139330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"astro-ph/0109241","last_updated":"2001-09-16T21:01:05Z","snapshot_observed_at":"2026-07-07T00:21:30.597730Z","submitted_at":"2001-09-16T21:01:05Z","title":"Radio Foregrounds for the 21cm Tomography of the Neutral Intergalactic Medium at High Redshifts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"astro-ph/0109241","snapshot_observed_at":"2026-08-03T21:30:51.198902Z","title":"Di Matteo, R","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.198902Z"},"links":{"cited_paper":"/paper/astro-ph/0109241","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:cc8236a8631c970442741c18a33d7b6d0af5a2cea656300fa37012f03ff87a88","observation_id":"38394ff6-d095-4f2a-9d5b-60d8e5f373ce","resolution":{"observed_at":"2026-08-03T21:30:51.198902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"astro-ph/0302099","last_updated":"2003-02-07T03:04:21Z","snapshot_observed_at":"2026-08-11T14:50:33.871200Z","submitted_at":"2003-02-05T21:28:52Z","title":"Foregrounds for 21cm Observations of Neutral Gas at High Redshift","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"astro-ph/0302099","snapshot_observed_at":"2026-08-03T21:30:51.265272Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.265272Z"},"links":{"cited_paper":"/paper/astro-ph/0302099","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:6f84f24107b0a1ae6bc25ff845da39b3398fa673b4aed1b92e7ebd654b909a74","observation_id":"fdd9cf4a-0012-4a76-b4e2-2ec0bbd58d11","resolution":{"observed_at":"2026-08-03T21:30:51.265272Z","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":"2026-08-03T21:30:51.324930Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.324930Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:32872657425e390686fac5a0ab842adb5e1a06cac6f80484ecd2a3d0e13614b1","observation_id":"33786fbb-1efb-4f65-a66c-5bcfe90058dd","resolution":{"observed_at":"2026-08-03T21:30:51.324930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0802.1525","last_updated":"2008-03-25T02:24:26Z","snapshot_observed_at":"2026-08-15T10:21:47.627665Z","submitted_at":"2008-02-12T20:36:52Z","title":"A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0802.1525","snapshot_observed_at":"2026-08-03T21:30:51.406741Z","title":"de Oliveira-Costa, M","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.406741Z"},"links":{"cited_paper":"/paper/0802.1525","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:50cc07e67e24aac42d758e93cd163afa98006dcdc71a9b2be54f9e2cdcef7a48","observation_id":"36cd8b22-09f4-4104-a744-8f2607adc2b6","resolution":{"observed_at":"2026-08-03T21:30:51.406741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.08211","last_updated":"2019-12-06T15:04:34Z","snapshot_observed_at":"2026-08-18T21:37:59.139671Z","submitted_at":"2019-07-18T18:00:03Z","title":"Data Analysis for Precision 21 cm Cosmology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.08211","snapshot_observed_at":"2026-08-03T21:30:51.491659Z","title":"Liu and J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.491659Z"},"links":{"cited_paper":"/paper/1907.08211","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:7d61b24ce98b39166b78758057ad325b4100398daf598b5668447df1bd375bd0","observation_id":"b035f141-54bb-42a2-bb70-483554dc5d2a","resolution":{"observed_at":"2026-08-03T21:30:51.491659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1103.0281","last_updated":"2011-05-13T21:09:03Z","snapshot_observed_at":"2026-08-15T04:53:20.564143Z","submitted_at":"2011-03-01T21:00:17Z","title":"A Method for 21cm Power Spectrum Estimation in the Presence of Foregrounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1103.0281","snapshot_observed_at":"2026-08-03T21:30:51.549998Z","title":"Liu and M","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.549998Z"},"links":{"cited_paper":"/paper/1103.0281","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:b86f46ef6cbe301622e9709b05829cf1e11e7e9d125e2033f5aa7b4873d05487","observation_id":"b97eb747-bc5c-4d61-a896-49dded2e3752","resolution":{"observed_at":"2026-08-03T21:30:51.549998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1504.07527","last_updated":"2016-04-18T19:58:58Z","snapshot_observed_at":"2026-08-14T22:50:40.460585Z","submitted_at":"2015-04-28T15:28:38Z","title":"Interpreting the unresolved intensity of cosmologically redshifted line radiation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1504.07527","snapshot_observed_at":"2026-08-03T21:30:51.631558Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.631558Z"},"links":{"cited_paper":"/paper/1504.07527","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:3c1f9fed21206703c35b3a75736bc6cd4852fa124a1a4f97a9a492b8f21dd275","observation_id":"fc8faf23-b1d3-4a06-a9b1-f0b26d13c37d","resolution":{"observed_at":"2026-08-03T21:30:51.631558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.14675","last_updated":"2023-02-01T07:17:47Z","snapshot_observed_at":"2026-08-17T19:36:19.945561Z","submitted_at":"2022-08-31T08:01:08Z","title":"A Semi-blind PCA-based Foreground Subtraction Method for 21 cm Intensity Mapping","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.14675","snapshot_observed_at":"2026-08-03T21:30:51.689576Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.689576Z"},"links":{"cited_paper":"/paper/2208.14675","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:64533b72d346c5e71a5f7bc23e2794455894509b1053fc10804fe8d4a18aa230","observation_id":"3bd6237e-9130-4322-b65b-d963ffb3e4e0","resolution":{"observed_at":"2026-08-03T21:30:51.689576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1201.2190","last_updated":"2012-05-24T09:34:33Z","snapshot_observed_at":"2026-08-16T02:44:08.375784Z","submitted_at":"2012-01-10T21:30:35Z","title":"Foreground Removal using FastICA: A Showcase of LOFAR-EoR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1201.2190","snapshot_observed_at":"2026-08-03T21:30:51.772522Z","title":"Chapman, F","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.772522Z"},"links":{"cited_paper":"/paper/1201.2190","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ce55e16853b298cb121a167beef30256b73e2c15d86bb3fb95eb73dedd8ea727","observation_id":"9c8675c3-d5a3-4479-b453-476d665a1b08","resolution":{"observed_at":"2026-08-03T21:30:51.772522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02907","last_updated":"2021-03-24T10:29:56Z","snapshot_observed_at":"2026-08-16T19:15:26.427693Z","submitted_at":"2020-10-06T17:37:48Z","title":"21cm foregrounds and polarization leakage: a user's guide on cleaning and mitigation strategies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02907","snapshot_observed_at":"2026-08-03T21:30:51.840870Z","title":"Cunnington, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.840870Z"},"links":{"cited_paper":"/paper/2010.02907","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:18835b2914eab06552c291f5d0388ce39fae9209e0fe630686d528257d43c36a","observation_id":"a5b3d7c6-1f68-454f-a57f-f835b1409b00","resolution":{"observed_at":"2026-08-03T21:30:51.840870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1209.4769","last_updated":"2013-01-10T14:21:05Z","snapshot_observed_at":"2026-08-16T00:14:06.082912Z","submitted_at":"2012-09-21T10:07:08Z","title":"The Scale of the Problem : Recovering Images of Reionization with GMCA","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1209.4769","snapshot_observed_at":"2026-08-03T21:30:51.918694Z","title":"Chapman, F","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:51.918694Z"},"links":{"cited_paper":"/paper/1209.4769","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:5bac55b9a6955cb08f8ddff0ea5aec8871088930c85ebe27dcd06337d0102762","observation_id":"c1b99e6a-f6c6-452d-b171-4c0c3853b3a3","resolution":{"observed_at":"2026-08-03T21:30:51.918694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.00742","last_updated":"2016-01-05T16:26:19Z","snapshot_observed_at":"2026-08-18T21:56:18.836624Z","submitted_at":"2015-09-02T15:31:10Z","title":"Extracting HI cosmological signal with Generalized Needlet Internal Linear Combination","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.00742","snapshot_observed_at":"2026-08-03T21:30:52.000595Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.000595Z"},"links":{"cited_paper":"/paper/1509.00742","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ffdeb85c6526fc178c88100999419aeb7fe8adfa2ee83d783bf7a35a8312773c","observation_id":"1fa83a60-6698-4da3-9a65-7f0baa6376cc","resolution":{"observed_at":"2026-08-03T21:30:52.000595Z","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":"2026-08-03T21:30:52.191541Z","title":"Marins, F","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.191541Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:37112d2a4cba234a4c2bd4f2977ba3150d6ecf431b625bb1d94bcb55d6ab19dd","observation_id":"b59cbaba-d5db-4ea9-851d-544826b0f206","resolution":{"observed_at":"2026-08-03T21:30:52.191541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.15892","last_updated":"2020-12-28T16:24:34Z","snapshot_observed_at":"2026-08-16T19:09:29.741878Z","submitted_at":"2020-10-29T19:13:41Z","title":"Gaussian Process Foreground Subtraction and Power Spectrum Estimation for 21 cm Cosmology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.15892","snapshot_observed_at":"2026-08-03T21:30:52.314362Z","title":"Kern and A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.314362Z"},"links":{"cited_paper":"/paper/2010.15892","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:32389fcab8b22beceac9a315c0312d816ae2edafcb67302989da0bb0b3207082","observation_id":"a4509dd7-5358-47a5-91e5-3f197e25494e","resolution":{"observed_at":"2026-08-03T21:30:52.314362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1302.0327","last_updated":"2013-02-02T01:00:05Z","snapshot_observed_at":"2026-08-15T00:30:12.262526Z","submitted_at":"2013-02-02T01:00:05Z","title":"All-Sky Interferometry with Spherical Harmonic Transit Telescopes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1302.0327","snapshot_observed_at":"2026-08-03T21:30:52.537179Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.537179Z"},"links":{"cited_paper":"/paper/1302.0327","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:dfd50b6b474e110cc4da29f997f3cc6c1ca35a3012ca2f0eddfbda1238a95be9","observation_id":"973e37c5-3f89-4c91-bb5c-86d2c9d96a5f","resolution":{"observed_at":"2026-08-03T21:30:52.537179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.04982","last_updated":"2015-06-16T14:11:44Z","snapshot_observed_at":"2026-08-14T22:44:13.595386Z","submitted_at":"2015-06-16T14:11:44Z","title":"A Bayesian analysis of redshifted 21-cm HI signal and foregrounds: Simulations for LOFAR","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.04982","snapshot_observed_at":"2026-08-03T21:30:52.711312Z","title":"Ghosh, L","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.711312Z"},"links":{"cited_paper":"/paper/1506.04982","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:e8ad8266888455a30be81f8e63a789db3c75e32ee858dee662c2cf2d79a0cb10","observation_id":"9553ca21-32ec-483f-b273-bd98cd2d6706","resolution":{"observed_at":"2026-08-03T21:30:52.711312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04146","last_updated":"2016-01-11T08:21:10Z","snapshot_observed_at":"2026-08-14T22:48:23.683331Z","submitted_at":"2015-05-15T18:11:43Z","title":"Bayesian semi-blind component separation for foreground removal in interferometric 21-cm observations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04146","snapshot_observed_at":"2026-08-03T21:30:52.785726Z","title":"Zhang, E","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.785726Z"},"links":{"cited_paper":"/paper/1505.04146","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:6ef6954792877026645bd7e6edc6fda60d67f91b1593d72c16d6186a77778f4d","observation_id":"e73cd873-bd98-4186-a14a-1270d94393f4","resolution":{"observed_at":"2026-08-03T21:30:52.785726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02608","last_updated":"2019-07-04T21:45:13Z","snapshot_observed_at":"2026-08-15T04:16:28.047863Z","submitted_at":"2019-07-04T21:45:13Z","title":"Joint estimation of the Epoch of Reionization power spectrum and foregrounds","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02608","snapshot_observed_at":"2026-08-03T21:30:52.898418Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:52.898418Z"},"links":{"cited_paper":"/paper/1907.02608","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:f5fad00c62d5406327e25c17eb9f819fb809a3d917c0e7ddd671b2e79dc86e3a","observation_id":"e8cc7e87-dcec-44e5-8fa8-3409cd46a1fd","resolution":{"observed_at":"2026-08-03T21:30:52.898418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04058","last_updated":"2023-02-08T13:52:33Z","snapshot_observed_at":"2026-08-16T15:56:49.197905Z","submitted_at":"2023-02-08T13:52:33Z","title":"All Sky Modelling Requirements for Bayesian 21 cm Power Spectrum Estimation with BayesEoR","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04058","snapshot_observed_at":"2026-08-03T21:30:53.035141Z","title":"Burba, P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.035141Z"},"links":{"cited_paper":"/paper/2302.04058","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:e9f42ebd5212b312835f47fbde21b9d93bc00bbf1831adfe5ccf02e0cd45d55b","observation_id":"b6891c81-93d2-4784-b1a0-2c9cd459ab0c","resolution":{"observed_at":"2026-08-03T21:30:53.035141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06682","last_updated":"2024-08-13T07:20:24Z","snapshot_observed_at":"2026-08-16T13:26:49.037147Z","submitted_at":"2024-08-13T07:20:24Z","title":"Correlation-based Beam Calibration of 21cm Intensity Mapping","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06682","snapshot_observed_at":"2026-08-03T21:30:53.099928Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.099928Z"},"links":{"cited_paper":"/paper/2408.06682","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:cd94b8a5a9c0ae13bea4b2f5210a01db31da207174bc07c232e313c0146a1453","observation_id":"4326ea88-ff5a-4016-bd54-e4a513d807a1","resolution":{"observed_at":"2026-08-03T21:30:53.099928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00833","last_updated":"2020-08-27T11:02:44Z","snapshot_observed_at":"2026-08-09T21:54:05.408796Z","submitted_at":"2020-01-03T13:44:44Z","title":"HIR4: cosmology from a simulated neutral hydrogen full sky using Horizon Run 4","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00833","snapshot_observed_at":"2026-08-03T21:30:53.172993Z","title":"Asorey, D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.172993Z"},"links":{"cited_paper":"/paper/2001.00833","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:be1ed5bd293d678a89c1449b9b8e1428a488b40edece6b38d6d4ef769c6de10b","observation_id":"5e73a2a4-ec36-486e-a2b1-d40c28e5c18e","resolution":{"observed_at":"2026-08-03T21:30:53.172993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.01407","last_updated":"2020-11-16T06:19:44Z","snapshot_observed_at":"2026-08-03T06:35:05.839393Z","submitted_at":"2020-06-02T06:07:23Z","title":"HIR4: Cosmological signatures imprinted on the cross correlation between 21-cm map and galaxy clustering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.01407","snapshot_observed_at":"2026-08-03T21:30:53.315739Z","title":"Shi, Y.-S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.315739Z"},"links":{"cited_paper":"/paper/2006.01407","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:f819f00cd5176052468dcefd7cf940dc7a0ce26e6235413bef49428cc98cb442","observation_id":"a2a54159-4f4b-4d6d-bd53-3bf1f47cc5c9","resolution":{"observed_at":"2026-08-03T21:30:53.315739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09278","last_updated":"2019-03-14T16:53:34Z","snapshot_observed_at":"2026-08-14T17:11:44.830991Z","submitted_at":"2019-02-25T14:13:09Z","title":"Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09278","snapshot_observed_at":"2026-08-03T21:30:53.438401Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.438401Z"},"links":{"cited_paper":"/paper/1902.09278","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:fdbe9b4dd9cbea48aed665dc0cafb409b2fc07709beeaba6deb5606aa0599387","observation_id":"39c9504f-31af-4b59-9b11-1a24d5474a23","resolution":{"observed_at":"2026-08-03T21:30:53.438401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.15843","last_updated":"2021-06-01T20:12:44Z","snapshot_observed_at":"2026-08-19T08:27:47.456940Z","submitted_at":"2020-10-29T18:00:02Z","title":"deep21: a Deep Learning Method for 21cm Foreground Removal","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.15843","snapshot_observed_at":"2026-08-03T21:30:53.537883Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.537883Z"},"links":{"cited_paper":"/paper/2010.15843","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ddadd2d587d09d46a966a7ce56e6a95d94b50c55077c7a8f39dfa6d789f0f109","observation_id":"2714fdb8-fb63-4977-9250-90b55c5d0fef","resolution":{"observed_at":"2026-08-03T21:30:53.537883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.08382","last_updated":"2024-02-26T12:45:49Z","snapshot_observed_at":"2026-08-16T18:45:08.201944Z","submitted_at":"2021-02-16T19:00:00Z","title":"Recovering the Wedge Modes Lost to 21-cm Foregrounds","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.08382","snapshot_observed_at":"2026-08-03T21:30:53.653431Z","title":"Gagnon-Hartman, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.653431Z"},"links":{"cited_paper":"/paper/2102.08382","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:2ecf63e5abc97006b77c389e34d61cb2666d1d9dedbe8d7fae54c16b59b08a76","observation_id":"6445c019-09d9-490b-8337-28b88cbe86b4","resolution":{"observed_at":"2026-08-03T21:30:53.653431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02780","last_updated":"2022-05-31T14:43:33Z","snapshot_observed_at":"2026-08-16T17:09:13.069897Z","submitted_at":"2022-04-05T10:36:20Z","title":"Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02780","snapshot_observed_at":"2026-08-03T21:30:53.761451Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.761451Z"},"links":{"cited_paper":"/paper/2204.02780","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:e90cf1fe7f2e86032bb7a8ee1388ff59971fe59baf404066fb762bd44626cbc0","observation_id":"84b073ab-9200-43cc-9288-0dda1a379e25","resolution":{"observed_at":"2026-08-03T21:30:53.761451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09740","last_updated":"2024-03-13T01:12:02Z","snapshot_observed_at":"2026-08-16T15:07:28.411249Z","submitted_at":"2023-08-18T18:00:01Z","title":"Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-$z$ galaxy mapping","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09740","snapshot_observed_at":"2026-08-03T21:30:53.846165Z","title":"Kennedy, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.846165Z"},"links":{"cited_paper":"/paper/2308.09740","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:c01d0b603b485416556d84709bc1450bdf5f0ce6a16065dfd71b921365e7e827","observation_id":"339787c9-30ee-4373-9131-f7dc238f5c14","resolution":{"observed_at":"2026-08-03T21:30:53.846165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02661","last_updated":"2024-02-28T09:37:18Z","snapshot_observed_at":"2026-08-16T15:42:28.381452Z","submitted_at":"2023-04-05T18:00:01Z","title":"Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02661","snapshot_observed_at":"2026-08-03T21:30:53.909054Z","title":"Bianco, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:53.909054Z"},"links":{"cited_paper":"/paper/2304.02661","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:1e60b3ef6a88d5f82e01d59d0c60fe19b6f1c58229d930d684b193545675191e","observation_id":"f7c72e08-529d-450d-8678-a34ddeb660b2","resolution":{"observed_at":"2026-08-03T21:30:53.909054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16814","last_updated":"2025-08-29T10:19:48Z","snapshot_observed_at":"2026-08-16T13:22:49.682474Z","submitted_at":"2024-08-29T18:00:00Z","title":"Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16814","snapshot_observed_at":"2026-08-03T21:30:54.002428Z","title":"Bianco, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.002428Z"},"links":{"cited_paper":"/paper/2408.16814","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:edbc0adfe51b65ca7e1ecaab588feac34764b57845c529b18121f6f54a29f75a","observation_id":"7ca8ec58-9030-47f1-b3c5-f4930b54012e","resolution":{"observed_at":"2026-08-03T21:30:54.002428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21097","last_updated":"2024-07-30T18:00:00Z","snapshot_observed_at":"2026-08-16T13:29:46.760710Z","submitted_at":"2024-07-30T18:00:00Z","title":"A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21097","snapshot_observed_at":"2026-08-03T21:30:54.089346Z","title":"Sabti, R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.089346Z"},"links":{"cited_paper":"/paper/2407.21097","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:2aba4e080556c4c3eadc69c3544aae85ac27657ca9941daaa2ce8e6b25738c9b","observation_id":"264c7f56-3c59-4fa1-8cfe-ee5ed81d8f74","resolution":{"observed_at":"2026-08-03T21:30:54.089346Z","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":"2026-08-03T21:30:54.177633Z","title":"Chen, K.-F","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.177633Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:68967bfc0182124a3960e6157318cb7c9d81ae6ae5bb79c4efa89487758d261d","observation_id":"0f3df9c6-1dcc-4c68-b96f-ea5a7fc61e0b","resolution":{"observed_at":"2026-08-03T21:30:54.177633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1405.1751","last_updated":"2014-09-16T09:26:29Z","snapshot_observed_at":"2026-08-14T23:34:52.513474Z","submitted_at":"2014-05-07T20:43:05Z","title":"Fast simulations for intensity mapping experiments","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1405.1751","snapshot_observed_at":"2026-08-03T21:30:54.263064Z","title":"Alonso, P","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.263064Z"},"links":{"cited_paper":"/paper/1405.1751","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:ea1dc8e41f9b27c1db46d945ad47c7751c54330365f45cddb9c2ccfa37c4e677","observation_id":"c8270351-8817-4844-93eb-43ee95e59ac9","resolution":{"observed_at":"2026-08-03T21:30:54.263064Z","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":"2026-08-03T21:30:54.384736Z","title":null,"venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.384736Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:1218f29afae28ae00d561b748eb82f99ac790646c755065d98d88c8709670eb5","observation_id":"82b8df25-5785-40e5-ab46-b70661964e3e","resolution":{"observed_at":"2026-08-03T21:30:54.384736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1207.3675","last_updated":"2012-07-16T13:29:24Z","snapshot_observed_at":"2026-08-15T12:14:04.295779Z","submitted_at":"2012-07-16T13:29:24Z","title":"The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1207.3675","snapshot_observed_at":"2026-08-03T21:30:54.485560Z","title":"Delabrouille, M","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.485560Z"},"links":{"cited_paper":"/paper/1207.3675","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:f4358524ef1979601ce11d0b58c8844c7899e1f34887aa939231d6c8a0be6548","observation_id":"09659df0-2172-4ea8-8d20-6cfdfb8569e2","resolution":{"observed_at":"2026-08-03T21:30:54.485560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.3628","last_updated":"2015-06-10T16:24:38Z","snapshot_observed_at":"2026-08-19T11:46:36.128895Z","submitted_at":"2014-10-17T20:00:11Z","title":"An improved source-subtracted and destriped 408 MHz all-sky map","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.3628","snapshot_observed_at":"2026-08-03T21:30:54.610947Z","title":"Remazeilles, C","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.610947Z"},"links":{"cited_paper":"/paper/1411.3628","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:81b4d24326fb69705a3a88369da8eda1d623532bf29e77fcc0175000d10d3cae","observation_id":"035ea748-2bc2-461c-9e53-acfc9fc613cd","resolution":{"observed_at":"2026-08-03T21:30:54.610947Z","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":"2026-08-03T21:30:54.730943Z","title":null,"venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.730943Z"},"links":{"citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:0d30184f2f05087e1e476c83734031ae36b1b306c5d02aacc1e85dc40dc8391a","observation_id":"433c25da-1381-46b7-b6ef-02522b898737","resolution":{"observed_at":"2026-08-03T21:30:54.730943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"astro-ph/0409513","last_updated":"2004-09-21T18:00:00Z","snapshot_observed_at":"2026-07-07T00:44:53.460945Z","submitted_at":"2004-09-21T18:00:00Z","title":"HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"astro-ph/0409513","snapshot_observed_at":"2026-08-03T21:30:54.853027Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.853027Z"},"links":{"cited_paper":"/paper/astro-ph/0409513","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:95bbd578fa27677d77a7d65268c4f26e431de4441e1173819d44d91d15162758","observation_id":"6c4c3938-8334-4c11-85c3-e564c7636dd5","resolution":{"observed_at":"2026-08-03T21:30:54.853027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.10815","last_updated":"2021-09-01T15:05:44Z","snapshot_observed_at":"2026-08-16T19:04:15.039916Z","submitted_at":"2020-11-21T16:22:13Z","title":"Hi intensity mapping with MeerKAT: Primary beam effects on foreground cleaning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.10815","snapshot_observed_at":"2026-08-03T21:30:54.955871Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:54.955871Z"},"links":{"cited_paper":"/paper/2011.10815","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:67c5c4ab6343b26be64ac812332b0feff5946bba2d90b0c2178be614a88431a8","observation_id":"56be1d50-f97b-4806-8c83-0ab0a882c100","resolution":{"observed_at":"2026-08-03T21:30:54.955871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.01785","last_updated":"2021-02-02T22:37:36Z","snapshot_observed_at":"2026-08-16T18:48:09.863995Z","submitted_at":"2021-02-02T22:37:36Z","title":"Toward a more stringent test of gravity with redshift space power spectrum: simultaneous probe of growth and amplitude of large-scale structure","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.01785","snapshot_observed_at":"2026-08-03T21:30:55.070941Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-03T21:30:55.070941Z"},"links":{"cited_paper":"/paper/2102.01785","citing_paper":"/paper/2511.15072"},"observation_digest":"sha256:86653f239d839ec52839be696f4c57243b19cbcdbc2fbc946a07b87ffc35394f","observation_id":"75a50b5f-6b6d-4beb-8884-f078373f1067","resolution":{"observed_at":"2026-08-03T21:30:55.070941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.15072","last_updated":"2026-06-22T01:48:03Z","latest_version":3,"primary_category":"astro-ph.CO","snapshot_observed_at":"2026-08-08T23:01:19.602215Z","submitted_at":"2025-11-19T03:24:01Z","title":"Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":68,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":68},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2511.15072."}