{"as_of":"2026-08-07T18:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34a7e072c14999a6f6d88595091d8943f518de372fe6df35bfa622e410ea1c9a","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:10:08.225125Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2506.08826/citation-record","integrity":"/paper/2506.08826/integrity","json":"/paper/2506.08826/citation-record.json","paper":"/paper/2506.08826"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"hep-ex/9411001","last_updated":"1994-11-08T17:21:14Z","snapshot_observed_at":"2026-07-07T03:38:34.362538Z","submitted_at":"1994-11-08T17:21:14Z","title":"Search for High Mass Top Quark Production in p anti-p Collisions at S**(1/2) = 1.8 TeV","version":1},"cited_work":{"arxiv_id":"hep-ex/9411001","doi":null,"metadata_source":"pith","pith_arxiv_id":"hep-ex/9411001","snapshot_observed_at":"2026-08-07T05:10:08.824578Z","title":"Search for High Mass Top Quark Production in p anti-p Collisions at S**(1/2) = 1.8 TeV","venue":"hep-ex","work_id":"9fdffb01-f74b-4eba-934c-f2b728445c0e","year":1994},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.044876Z"},"links":{"cited_paper":"/paper/hep-ex/9411001","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:b7c6f6dfbe90b6406e7c6c3b3f698035edba2fe007d3b8f5850d2b0c4a1da47b","observation_id":"873252cc-f1f6-4e79-95d2-bccb06524d51","resolution":{"observed_at":"2026-08-07T05:10:08.830731Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:10:08.050828Z","title":"Data analysis in high energy physics: a practical guide to statistical methods","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.050828Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:f62414b8b7ce520f1506f2dfdc83ebb56b399718a6d4a796af7bb2456b6afc44","observation_id":"5eccbd29-0177-474a-9c16-45d44c7a9416","resolution":{"observed_at":"2026-08-07T05:10:08.050828Z","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":"2026-08-07T05:10:08.909578Z","title":"Background estimation with the ABCD method featuring the TRooFit toolkit","venue":null,"work_id":"76041c91-28c4-40e3-91eb-38913a346332","year":2018},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.055886Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:be8f72576176fb50db4aa825ecfa525c9fb41d78e47408da7a103b4e3e499385","observation_id":"6fe724c1-d8be-4ba7-860c-1f5f7f913693","resolution":{"observed_at":"2026-08-07T05:10:08.914643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.14400","last_updated":"2020-07-28T18:00:01Z","snapshot_observed_at":"2026-07-31T19:00:21.441734Z","submitted_at":"2020-07-28T18:00:01Z","title":"ABCDisCo: Automating the ABCD Method with Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.14400","snapshot_observed_at":"2026-08-07T05:10:08.061258Z","title":"Automating the ABCD method with machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.061258Z"},"links":{"cited_paper":"/paper/2007.14400","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:213c29a0b807e74aaf3b10448b145169bdd93af952a1cc8f1789aff816745ae1","observation_id":"21ce5714-86e3-460e-a300-07830aae1592","resolution":{"observed_at":"2026-08-07T05:10:08.061258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0803.4101","last_updated":"2008-03-28T12:35:10Z","snapshot_observed_at":"2026-07-06T01:35:49.862060Z","submitted_at":"2008-03-28T12:35:10Z","title":"Measuring and testing dependence by correlation of distances","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0803.4101","snapshot_observed_at":"2026-08-07T05:10:08.067579Z","title":"Measuring and testing dependence by correlation of distances","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.067579Z"},"links":{"cited_paper":"/paper/0803.4101","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:040ecd897bd1ec6ee8dff41030b7447a6d280303846fd4b7e24e04a410007b79","observation_id":"744459cb-7f23-4201-92a9-28ff626099fa","resolution":{"observed_at":"2026-08-07T05:10:08.067579Z","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":"2026-08-07T05:10:08.072994Z","title":"The CMS experiment at the CERN LHC","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.072994Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:bb5744d03bdc48ee5db8a02c8663d418d949d077ed1321dd366aeb9f39edc582","observation_id":"40948730-49c5-44b0-aba5-8c773b0f1026","resolution":{"observed_at":"2026-08-07T05:10:08.072994Z","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":"2026-08-07T05:10:08.078477Z","title":"LHC Machine","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.078477Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:dc3da791d189c2a9f6fe0b73bb970b4af38b1c11281143b904410bc861282448","observation_id":"ef0dea6c-d9d5-4ee3-9ebd-d7a8d4b4a816","resolution":{"observed_at":"2026-08-07T05:10:08.078477Z","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":"2026-08-07T05:10:08.893459Z","title":"Search for top squarks in final states with many light-flavor jets and 0, 1, or 2 charged leptons in proton-proton collisions at √s=13 TeV","venue":null,"work_id":"89a87d7b-a47b-47ce-871c-02c7f557fbfd","year":2025},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.083123Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:5cdaa1ebd750ad9c97aef8eafda7cb4c0ebb92291a50756929fa71c391c01324","observation_id":"0fe5fbb8-3d02-4c80-9586-8a901bd24ef7","resolution":{"observed_at":"2026-08-07T05:10:08.898570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:10:08.876507Z","title":"Constrained differential optimization","venue":null,"work_id":"a74b1914-c182-47dc-a3c8-de7eeecd4809","year":1987},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.088221Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:8a1f867beb0e9bf63ad9ba7350708f03187c2ed40c16e05aa3d62c809701b7d7","observation_id":"fd42e890-2d37-46de-95eb-383dd6df7eaa","resolution":{"observed_at":"2026-08-07T05:10:08.881536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:10:08.858796Z","title":"Note on regression and inheritance in the case of two parents","venue":null,"work_id":"7a0d3e07-09e6-4f84-940a-b59b3670e067","year":null},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.093248Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:4f84eae50009f2cdd35e735b5ef90eee5d8c93dbf578e976764c48525bea2361","observation_id":"3589d3de-573e-435a-bcc2-7c29afbc98ae","resolution":{"observed_at":"2026-08-07T05:10:08.865391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-540-88908-3_9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:08.273815Z","title":"Visualizing the Pareto frontier","venue":null,"work_id":"d9418dc8-465c-4b2a-af97-6e97b12f56f8","year":2008},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.098487Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:2bc8f3fbd56ec55a4230ad5446ff31c2a740ee5fbd9d1e539440b97784531687","observation_id":"94ecf6cb-dabb-4c22-b0eb-039103503fe5","resolution":{"observed_at":"2026-08-07T05:10:08.409208Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1105.5135","last_updated":"2011-05-25T20:00:02Z","snapshot_observed_at":"2026-07-06T02:28:17.235549Z","submitted_at":"2011-05-25T20:00:02Z","title":"Stealth Supersymmetry","version":1},"cited_work":{"arxiv_id":"1105.5135","doi":null,"metadata_source":"pith","pith_arxiv_id":"1105.5135","snapshot_observed_at":"2026-08-07T05:10:08.769815Z","title":"Stealth Supersymmetry","venue":"hep-ph","work_id":"c4069285-09ea-493e-9192-fb577101d209","year":2011},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.103952Z"},"links":{"cited_paper":"/paper/1105.5135","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:7a320c6036e3d2744f41718c5c818d6bd70acc8275205599a618539e337fd42a","observation_id":"e84827c2-1d93-432b-897b-35279eff740d","resolution":{"observed_at":"2026-08-07T05:10:08.774782Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1201.4875","last_updated":"2012-01-23T21:28:15Z","snapshot_observed_at":"2026-07-06T02:41:38.149023Z","submitted_at":"2012-01-23T21:28:15Z","title":"A Stealth Supersymmetry Sampler","version":1},"cited_work":{"arxiv_id":"1201.4875","doi":null,"metadata_source":"pith","pith_arxiv_id":"1201.4875","snapshot_observed_at":"2026-08-07T05:10:08.747880Z","title":"A Stealth Supersymmetry Sampler","venue":"hep-ph","work_id":"d9e6abb9-dde6-4d5c-9bd4-9fe6f36f9013","year":2012},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.112387Z"},"links":{"cited_paper":"/paper/1201.4875","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:d9a86db9058e173e12b971f2fa6b4af3f05ae21e7b08bc84026fa2a063dbdad6","observation_id":"7f1fe3b5-d765-4032-9fed-88ad8cbdb0a6","resolution":{"observed_at":"2026-08-07T05:10:08.753095Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.05781","last_updated":"2015-12-17T21:00:01Z","snapshot_observed_at":"2026-07-06T04:40:19.664205Z","submitted_at":"2015-12-17T21:00:01Z","title":"Stealth Supersymmetry Simplified","version":1},"cited_work":{"arxiv_id":"1512.05781","doi":null,"metadata_source":"pith","pith_arxiv_id":"1512.05781","snapshot_observed_at":"2026-08-07T05:10:08.725448Z","title":"Stealth Supersymmetry Simplified","venue":"hep-ph","work_id":"bd1cef06-f6e6-4336-9ed0-f0f245dcbbdf","year":2015},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.119549Z"},"links":{"cited_paper":"/paper/1512.05781","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:af128e309d9153975724ebad871c990cdd1e095cc06a1abe2f77752d281cb6e1","observation_id":"481befb9-9be7-4943-951c-7024dadf3281","resolution":{"observed_at":"2026-08-07T05:10:08.730940Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06976","last_updated":"2021-08-21T12:27:47Z","snapshot_observed_at":"2026-07-06T10:41:07.569038Z","submitted_at":"2021-02-13T18:25:49Z","title":"Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at $\\sqrt{s} =$ 13 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06976","snapshot_observed_at":"2026-08-07T05:10:08.126697Z","title":"Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at √s=13 TeV","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.126697Z"},"links":{"cited_paper":"/paper/2102.06976","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:b8e026c0e57ff537534be9f832ee5d948b1c1bce870373d5e57e15f8fcd9f0c4","observation_id":"d730f070-03fb-4d9e-9f4c-ac08ec112a95","resolution":{"observed_at":"2026-08-07T05:10:08.126697Z","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":"2026-08-07T05:10:08.841782Z","title":"Chollet et al., “Keras”, 2015.https://keras.io","venue":null,"work_id":"19a87f37-f2b9-4d09-9535-d562351c436d","year":2015},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.132884Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:ed72bb7f52314c0a0a287d32cfbc842e50bd6e1dac3a3d6f60ced526e0e499f6","observation_id":"96b3d74f-318d-4225-bd6e-5a47e9e2a151","resolution":{"observed_at":"2026-08-07T05:10:08.847406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08695","last_updated":"2016-05-31T19:46:10Z","snapshot_observed_at":"2026-08-07T14:23:17.015812Z","submitted_at":"2016-05-27T15:49:50Z","title":"TensorFlow: A system for large-scale machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08695","snapshot_observed_at":"2026-08-07T05:10:08.138713Z","title":"TensorFlow: A system for large-scale machine learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.138713Z"},"links":{"cited_paper":"/paper/1605.08695","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:c6f3660420c4370084cce7ccb3ffe08171aacbcd5b39f4cb32d041b2fdc60c5a","observation_id":"aa02f009-a2ef-4285-a467-db2766b38517","resolution":{"observed_at":"2026-08-07T05:10:08.138713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-07T05:10:08.144866Z","title":"PyTorch: an imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.144866Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:8482522b9b035824ebb58c275f1228eecb09ce18b8545b6a8cadcc3c408fe762","observation_id":"e4fddb1f-b122-4308-abbf-72ed2e92e5e6","resolution":{"observed_at":"2026-08-07T05:10:08.144866Z","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":"2026-07-06T02:11:23.670680Z","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":"2026-08-07T05:10:08.150897Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.150897Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:bb129d1f58af7d48dea076f90f52556a8615d50121bbe13f2f5a6b6764f8afe8","observation_id":"ce830f58-565f-4e3c-bed0-06d1ba8e974c","resolution":{"observed_at":"2026-08-07T05:10:08.150897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"hep-ph/0409146","last_updated":"2004-09-13T15:09:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2004-09-13T15:09:18Z","title":"A New Method for Combining NLO QCD with Shower Monte Carlo Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"hep-ph/0409146","snapshot_observed_at":"2026-08-07T05:10:08.156018Z","title":"A new method for combining NLO QCD with shower Monte Carlo algorithms","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.156018Z"},"links":{"cited_paper":"/paper/hep-ph/0409146","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:f11f2bb96f54a375c533f49e1b5ccabc0eab3da63be2654cc591584fdea95b59","observation_id":"216c0228-6d77-477b-8b36-974372b147c0","resolution":{"observed_at":"2026-08-07T05:10:08.156018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0709.2092","last_updated":"2007-09-13T17:49:16Z","snapshot_observed_at":"2026-08-07T18:07:31.311870Z","submitted_at":"2007-09-13T17:49:16Z","title":"Matching NLO QCD computations with Parton Shower simulations: the POWHEG method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0709.2092","snapshot_observed_at":"2026-08-07T05:10:08.162282Z","title":"Matching NLO QCD computations with parton shower simulations: the POWHEG method","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.162282Z"},"links":{"cited_paper":"/paper/0709.2092","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:ff81c768212d8af694f00c9e71ffec42d6641552b41f208576b44ed52b812dcd","observation_id":"b08b29b0-1a98-4bae-8ff2-959a1f3a6ef6","resolution":{"observed_at":"2026-08-07T05:10:08.162282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1002.2581","last_updated":"2010-02-12T16:07:27Z","snapshot_observed_at":"2026-07-06T02:06:43.898500Z","submitted_at":"2010-02-12T16:07:27Z","title":"A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1002.2581","snapshot_observed_at":"2026-08-07T05:10:08.168071Z","title":"A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.168071Z"},"links":{"cited_paper":"/paper/1002.2581","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:d1111a4af863d0f0b2d7da92f13607c14786fef987c313e8b94a94879d0d9bb6","observation_id":"4c316d31-ffff-4301-a172-cb6d71bfa364","resolution":{"observed_at":"2026-08-07T05:10:08.168071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0707.3088","last_updated":"2007-09-22T14:46:39Z","snapshot_observed_at":"2026-07-06T01:29:38.477605Z","submitted_at":"2007-07-20T15:04:38Z","title":"A Positive-Weight Next-to-Leading-Order Monte Carlo for Heavy Flavour Hadroproduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0707.3088","snapshot_observed_at":"2026-08-07T05:10:08.173191Z","title":"A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.173191Z"},"links":{"cited_paper":"/paper/0707.3088","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:39c17e3df0c1e49b6fce9b090fb26bd2cbfb9f064d7b6aadbf0ef6ea5765af58","observation_id":"3013f4bd-cfe5-4eef-ac72-f8d51da228cf","resolution":{"observed_at":"2026-08-07T05:10:08.173191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1112.5675","last_updated":"2013-03-27T17:28:42Z","snapshot_observed_at":"2026-08-03T00:06:09.978819Z","submitted_at":"2011-12-23T23:40:35Z","title":"Top++: a program for the calculation of the top-pair cross-section at hadron colliders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1112.5675","snapshot_observed_at":"2026-08-07T05:10:08.178172Z","title":"Top++: A program for the calculation of the top-pair cross-section at hadron colliders","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.178172Z"},"links":{"cited_paper":"/paper/1112.5675","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:c176c04fc08d2bf4a586e9af7842111cbca69128a28070d6b0ab468861f2f7c2","observation_id":"c3d696f6-e149-4330-913c-925fc4fca02f","resolution":{"observed_at":"2026-08-07T05:10:08.178172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1405.0301","last_updated":"2014-07-21T09:46:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-05-01T20:24:46Z","title":"The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1405.0301","snapshot_observed_at":"2026-08-07T05:10:08.183089Z","title":"The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.183089Z"},"links":{"cited_paper":"/paper/1405.0301","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:c0b96786ce20c7d599bb649594eac3f6de90628fb00a68660cd56da361776d46","observation_id":"8d908cef-5948-4249-8d87-865312b44395","resolution":{"observed_at":"2026-08-07T05:10:08.183089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1407.5066","last_updated":"2014-11-26T10:20:12Z","snapshot_observed_at":"2026-07-06T03:49:27.130621Z","submitted_at":"2014-07-18T17:31:24Z","title":"Squark and gluino production cross sections in pp collisions at $\\sqrt{s}$ = 13, 14, 33 and 100 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1407.5066","snapshot_observed_at":"2026-08-07T05:10:08.189861Z","title":"Squark and gluino production cross sections in pp collisions at√s=13, 14, 33 and 100 TeV","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.189861Z"},"links":{"cited_paper":"/paper/1407.5066","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:4fd84f8ef4f583e218e647935acf39253d62e8d68637bdb46c0bd67e6c8cd9c9","observation_id":"06cfac79-735e-4de2-9762-b02c8afc230c","resolution":{"observed_at":"2026-08-07T05:10:08.189861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.07741","last_updated":"2016-07-26T15:11:41Z","snapshot_observed_at":"2026-07-06T05:04:58.068641Z","submitted_at":"2016-07-26T15:11:41Z","title":"NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.07741","snapshot_observed_at":"2026-08-07T05:10:08.195443Z","title":"NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.195443Z"},"links":{"cited_paper":"/paper/1607.07741","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:1ecbebff384ad7de2a4b6153024d62aed3e5289c8851c22b453367b4d109ac73","observation_id":"73906fcf-0ac0-48cf-beb5-2e83f464f108","resolution":{"observed_at":"2026-08-07T05:10:08.195443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1410.3012","last_updated":"2014-10-11T17:01:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-10-11T17:01:19Z","title":"An Introduction to PYTHIA 8.2","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1410.3012","snapshot_observed_at":"2026-08-07T05:10:08.201214Z","title":"An introduction to PYTHIA 8.2","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.201214Z"},"links":{"cited_paper":"/paper/1410.3012","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:30df41607b6e5704ab60530bed4d6a012899476077871dbf1d7f0228d7546708","observation_id":"3fca7983-c2e0-4efd-9a7c-149112f0ecd2","resolution":{"observed_at":"2026-08-07T05:10:08.201214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.00428","last_updated":"2017-09-22T16:31:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-01T18:00:01Z","title":"Parton distributions from high-precision collider data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.00428","snapshot_observed_at":"2026-08-07T05:10:08.208636Z","title":"Parton distributions from high-precision collider data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.208636Z"},"links":{"cited_paper":"/paper/1706.00428","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:1f8d883db0c48f5a6bbd531811439e82de0b94e2ca30c282cd3cafbf048dbd43","observation_id":"d56d22b0-9f9f-418f-9b9e-94459bd18a74","resolution":{"observed_at":"2026-08-07T05:10:08.208636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.12179","last_updated":"2020-01-05T21:49:38Z","snapshot_observed_at":"2026-08-05T21:36:34.054502Z","submitted_at":"2019-03-28T16:29:09Z","title":"Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.12179","snapshot_observed_at":"2026-08-07T05:10:08.214593Z","title":"Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.214593Z"},"links":{"cited_paper":"/paper/1903.12179","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:4c554a2d0f6ce3ba5d3c2d719f4b6a62d4df8b5698b42857b0f15caa0e55ae69","observation_id":"adcf1332-596f-42dc-b7b3-544c7a3c391d","resolution":{"observed_at":"2026-08-07T05:10:08.214593Z","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":"2026-08-07T05:10:08.219762Z","title":"GEANT4—a simulation toolkit","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.219762Z"},"links":{"citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:42d3e8555f66bafc66849637d3522920064746d488cf0074949f80268e267dd5","observation_id":"8dd35076-0da7-4066-bd7c-56fb860ee810","resolution":{"observed_at":"2026-08-07T05:10:08.219762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.10831","last_updated":"2021-07-03T22:38:37Z","snapshot_observed_at":"2026-08-06T14:42:19.295575Z","submitted_at":"2019-06-26T03:51:49Z","title":"Improved Extrapolation Methods of Data-driven Background Estimation in High-Energy Physics","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.10831","snapshot_observed_at":"2026-08-07T05:10:08.225125Z","title":"Improved extrapolation methods of data-driven background estimations in high energy physics","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:08.225125Z"},"links":{"cited_paper":"/paper/1906.10831","citing_paper":"/paper/2506.08826"},"observation_digest":"sha256:fc6db2f14b72b46d255870fe415c7b3336c7847b5cdbbeec977763e57984894a","observation_id":"e3e7a955-bd32-4dcd-a030-9c51d2fd8b11","resolution":{"observed_at":"2026-08-07T05:10:08.225125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.08826","last_updated":"2025-06-10T14:15:46Z","latest_version":1,"primary_category":"hep-ex","snapshot_observed_at":"2026-08-07T04:58:59.435106Z","submitted_at":"2025-06-10T14:15:46Z","title":"Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":5,"verified_fuzzy":5},"total_outbound_references":32},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.08826."}