{"as_of":"2026-08-22T06:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7b003b43f5c6e04d1a096d2dd1419795b8eed0b8a8d317ade49e1f019e008ab","coverage":[{"denominator":93,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":93,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T21:19:44.409630Z","state":"measured"},{"denominator":93,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":93,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2607.16144/citation-record","integrity":"/paper/2607.16144/integrity","json":"/paper/2607.16144/citation-record.json","paper":"/paper/2607.16144"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1410.3012","last_updated":"2014-10-11T17:01:19Z","snapshot_observed_at":"2026-08-14T12:01:16.893295Z","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":"2026-08-01T21:19:35.279859Z","title":"Sj¨ ostrand, S","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.279859Z"},"links":{"cited_paper":"/paper/1410.3012","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:02ef689b55a81d7dcc9094ca9393ebdb028e5911b8bd16d0c35f7bb1b0c0440c","observation_id":"7b9aa154-e9a1-45b6-b17d-fddd33afdfc4","resolution":{"observed_at":"2026-08-01T21:19:35.279859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.01178","last_updated":"2015-12-03T18:17:02Z","snapshot_observed_at":"2026-08-14T22:20:07.425525Z","submitted_at":"2015-12-03T18:17:02Z","title":"Herwig 7.0 / Herwig++ 3.0 Release Note","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.01178","snapshot_observed_at":"2026-08-01T21:19:35.329410Z","title":"Bellmet al., Herwig 7.0/Herwig++ 3.0 release note, Eur","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.329410Z"},"links":{"cited_paper":"/paper/1512.01178","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:91cce2ade6b007b17a3703560a8437ae694f0212f7ceeba840622f33a1ef6f28","observation_id":"4820fdc8-d90a-4a35-aa2d-736daa4d7948","resolution":{"observed_at":"2026-08-01T21:19:35.329410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.09127","last_updated":"2019-09-03T17:41:38Z","snapshot_observed_at":"2026-08-21T04:37:04.256928Z","submitted_at":"2019-05-22T13:30:53Z","title":"Event Generation with Sherpa 2.2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.09127","snapshot_observed_at":"2026-08-01T21:19:35.419744Z","title":"Bothmannet al.(Sherpa), Event Generation with Sherpa 2.2, SciPost Phys.7, 034 (2019), arXiv:1905.09127 [hep-ph]","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.419744Z"},"links":{"cited_paper":"/paper/1905.09127","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:518ec02bfa02d609cc62c935700eabd014cfa60610718dccb6b95d1c23c87154","observation_id":"4956a18f-70f3-4f6a-b30c-e4204aae58b6","resolution":{"observed_at":"2026-08-01T21:19:35.419744Z","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":"2026-08-14T01:28:40.957968Z","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":"2026-08-01T21:19:35.494356Z","title":"Alwall, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.494356Z"},"links":{"cited_paper":"/paper/1405.0301","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:54c2e64be1bdc2447c0406484f31674a7173d2ed97107eb060a950e75c0a3899","observation_id":"9488488f-303b-4035-8167-61cfc37dcaee","resolution":{"observed_at":"2026-08-01T21:19:35.494356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"hep-ph/0204244","last_updated":"2002-07-12T07:17:27Z","snapshot_observed_at":"2026-08-03T05:17:46.619889Z","submitted_at":"2002-04-22T12:45:48Z","title":"Matching NLO QCD computations and parton shower simulations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"hep-ph/0204244","snapshot_observed_at":"2026-08-01T21:19:35.550748Z","title":"Frixione and B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.550748Z"},"links":{"cited_paper":"/paper/hep-ph/0204244","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:d62068b9d01a7e83eeb86470e18e66d7d40953836f44aa69c98ee7b786aa1871","observation_id":"a57cfd9b-d614-46d7-890d-2b8514413d5e","resolution":{"observed_at":"2026-08-01T21:19:35.550748Z","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":"2026-08-16T17:19:22.546613Z","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":"2026-08-01T21:19:35.705543Z","title":"Alioli, P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.705543Z"},"links":{"cited_paper":"/paper/1002.2581","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:1154ddd50e06d3953c7c98c630f68e915ca72afb140d2a08219612666a5cb5bf","observation_id":"bc319de4-00b8-40f3-80f7-583055d5fbb8","resolution":{"observed_at":"2026-08-01T21:19:35.705543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0708.4233","last_updated":"2011-11-15T15:30:22Z","snapshot_observed_at":"2026-08-18T17:29:07.556997Z","submitted_at":"2007-08-30T21:07:10Z","title":"WHIZARD: Simulating Multi-Particle Processes at LHC and ILC","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0708.4233","snapshot_observed_at":"2026-08-01T21:19:35.868028Z","title":"Kilian, T","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.868028Z"},"links":{"cited_paper":"/paper/0708.4233","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:ff5e04dc45927cae5908ed5314f67d1d5d66d95820a8830044bb7fac0356d8b1","observation_id":"39f7b350-0789-4ca1-b6a6-10f58514316c","resolution":{"observed_at":"2026-08-01T21:19:35.868028Z","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":"2026-08-01T21:19:35.987814Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:35.987814Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e8e9be9193ac98026e0a981116ad9aee8cb898432a336598d809739ed664d3e7","observation_id":"a2411077-8e2b-4089-8f33-a17a34b840d3","resolution":{"observed_at":"2026-08-01T21:19:35.987814Z","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":"2026-08-01T21:19:36.196286Z","title":"Denby, Neural Networks and Cellular Automata in Experimental High-energy Physics, Comput","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.196286Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:cfef7a562b4765fdd990cf63d7c0941daeda0e2b881d6b4a0e4008827015241f","observation_id":"7ad16a03-1d92-4e9c-a29c-02b840488116","resolution":{"observed_at":"2026-08-01T21:19:36.196286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.13681","last_updated":"2020-10-21T09:12:54Z","snapshot_observed_at":"2026-08-13T22:00:44.733715Z","submitted_at":"2020-07-27T16:49:57Z","title":"Graph Neural Networks in Particle Physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.13681","snapshot_observed_at":"2026-08-01T21:19:36.257787Z","title":"Shlomi, P","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.257787Z"},"links":{"cited_paper":"/paper/2007.13681","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:a67096c7caa077bd36a0ff8cb8d4804288efd3ce8d599a6986e070be4560ef78","observation_id":"4f72cf91-59d8-45c7-99e7-80a0b9f834ab","resolution":{"observed_at":"2026-08-01T21:19:36.257787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06995","last_updated":"2021-09-21T18:30:52Z","snapshot_observed_at":"2026-08-16T18:39:29.118030Z","submitted_at":"2021-03-11T23:10:18Z","title":"Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06995","snapshot_observed_at":"2026-08-01T21:19:36.313713Z","title":"Juet al.(Exa.TrkX), Performance of a geomet- ric deep learning pipeline for HL-LHC particle track- ing, Eur","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.313713Z"},"links":{"cited_paper":"/paper/2103.06995","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:60f6e04d2b1cf190574244ca493590de2492ca07c79a3ac912315da134215df4","observation_id":"a3048289-9db0-4d3a-9810-71d19158d2d7","resolution":{"observed_at":"2026-08-01T21:19:36.313713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.03772","last_updated":"2024-01-29T15:22:51Z","snapshot_observed_at":"2026-08-19T20:21:24.909763Z","submitted_at":"2022-02-08T10:36:29Z","title":"Particle Transformer for Jet Tagging","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.03772","snapshot_observed_at":"2026-08-01T21:19:36.402246Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.402246Z"},"links":{"cited_paper":"/paper/2202.03772","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:2ad3fca2dad3bdd500dc1d08d7c6f8e1a5e4b074810fbb6712996f54887d3b8c","observation_id":"08bdc296-12fc-4156-bfee-e12ab68143f2","resolution":{"observed_at":"2026-08-01T21:19:36.402246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.02770","last_updated":"2021-02-02T04:39:40Z","snapshot_observed_at":"2026-08-20T11:49:01.703167Z","submitted_at":"2021-02-02T04:39:40Z","title":"A Living Review of Machine Learning for Particle Physics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.02770","snapshot_observed_at":"2026-08-01T21:19:36.503572Z","title":"Feickert and B","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.503572Z"},"links":{"cited_paper":"/paper/2102.02770","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:2ff4079614aef98bbc763ad0e42d9d5ee46fb73c9d820bbde2ede10d892c432b","observation_id":"564d397d-d1c3-4ead-8aa7-0ece23578f59","resolution":{"observed_at":"2026-08-01T21:19:36.503572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.02355","last_updated":"2017-12-21T22:27:48Z","snapshot_observed_at":"2026-08-17T13:06:49.843948Z","submitted_at":"2017-05-05T18:20:47Z","title":"Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.02355","snapshot_observed_at":"2026-08-01T21:19:36.660051Z","title":"Paganini, L","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.660051Z"},"links":{"cited_paper":"/paper/1705.02355","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:2830a687355b73809bfa1043447bb4cf8956f08b34b7cc5617295a6431135929","observation_id":"bb6b21a6-8235-4294-b6a3-fa9b05e775e2","resolution":{"observed_at":"2026-08-01T21:19:36.660051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.05285","last_updated":"2023-05-05T08:28:28Z","snapshot_observed_at":"2026-08-19T21:06:02.913216Z","submitted_at":"2021-06-09T18:00:02Z","title":"CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.05285","snapshot_observed_at":"2026-08-01T21:19:36.766372Z","title":"Krause and D","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.766372Z"},"links":{"cited_paper":"/paper/2106.05285","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:0c347182c745a49ef3bdd378a1bc2881994715645839d66b03c945713c83a93c","observation_id":"0cde05fd-3f63-463f-8538-9372cd6c0375","resolution":{"observed_at":"2026-08-01T21:19:36.766372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.11898","last_updated":"2022-10-19T23:59:07Z","snapshot_observed_at":"2026-08-19T20:52:57.431023Z","submitted_at":"2022-06-17T18:01:02Z","title":"Score-based Generative Models for Calorimeter Shower Simulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.11898","snapshot_observed_at":"2026-08-01T21:19:36.830475Z","title":"Mikuni and B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.830475Z"},"links":{"cited_paper":"/paper/2206.11898","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:8efafe047ab5fb09a7bcfd719dd4fc575e4ac2b5f43de1d50ac0cd93315ba80d","observation_id":"1373aa3f-67ed-467c-bfc6-7d5a2b56252c","resolution":{"observed_at":"2026-08-01T21:19:36.830475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03876","last_updated":"2023-10-02T18:18:45Z","snapshot_observed_at":"2026-08-20T04:28:39.366060Z","submitted_at":"2023-08-07T19:09:33Z","title":"Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03876","snapshot_observed_at":"2026-08-01T21:19:36.969355Z","title":"Amram and K","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:36.969355Z"},"links":{"cited_paper":"/paper/2308.03876","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:7dff8232c63f005853444a30e0595e7c167a812689f14cc73e719d2c55fc31da","observation_id":"7ff50389-69f9-4028-8a11-28d59b4fb38d","resolution":{"observed_at":"2026-08-01T21:19:36.969355Z","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":"2026-08-01T21:19:37.063000Z","title":"Amramet al., CaloChallenge 2022: a community chal- lenge for fast calorimeter simulation, Rept","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.063000Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:1f08fa77fdcf8b3645e40ab6bdb1328ff8bade6088595686fd8fc741ed0a7f4d","observation_id":"8f9e8b8e-ae48-45c0-9c61-a895185b9593","resolution":{"observed_at":"2026-08-01T21:19:37.063000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02551","last_updated":"2024-11-22T07:20:51Z","snapshot_observed_at":"2026-08-16T17:58:33.890758Z","submitted_at":"2021-09-06T15:40:27Z","title":"AtlFast3: the next generation of fast simulation in ATLAS","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02551","snapshot_observed_at":"2026-08-01T21:19:37.152016Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.152016Z"},"links":{"cited_paper":"/paper/2109.02551","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e5dd1a796d7d928e04cbdb5c6e21fe79a252625ef07af567767b162cded5ddba","observation_id":"c61f9c6b-9f97-4cdc-8146-c555800d21a1","resolution":{"observed_at":"2026-08-01T21:19:37.152016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.03764","last_updated":"2019-11-06T16:55:36Z","snapshot_observed_at":"2026-08-14T16:07:47.127509Z","submitted_at":"2019-07-08T18:00:01Z","title":"How to GAN LHC Events","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.03764","snapshot_observed_at":"2026-08-01T21:19:37.201897Z","title":"Butter, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.201897Z"},"links":{"cited_paper":"/paper/1907.03764","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:acaf3d0372591dd9546b796f5c79f03104d23a6a3c70e7ee7e4ee44ebb8f6d8c","observation_id":"c6a2e200-8e41-4668-8a34-7ab09e66ce29","resolution":{"observed_at":"2026-08-01T21:19:37.201897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.07460","last_updated":"2022-12-28T12:29:21Z","snapshot_observed_at":"2026-08-16T17:14:37.724748Z","submitted_at":"2022-03-14T19:36:46Z","title":"Machine Learning and LHC Event Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.07460","snapshot_observed_at":"2026-08-01T21:19:37.237631Z","title":"Butter, T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.237631Z"},"links":{"cited_paper":"/paper/2203.07460","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:7f6b893de444d6f130af19c7606ecdc7a45e11e075686c63e6824c63e91bfc24","observation_id":"5bbee6cf-05cc-43f9-b3a7-899ef6c21caa","resolution":{"observed_at":"2026-08-01T21:19:37.237631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05376","last_updated":"2024-02-21T13:38:39Z","snapshot_observed_at":"2026-08-16T15:49:27.627586Z","submitted_at":"2023-03-09T16:23:49Z","title":"PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05376","snapshot_observed_at":"2026-08-01T21:19:37.294367Z","title":"Leigh, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.294367Z"},"links":{"cited_paper":"/paper/2303.05376","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:6dbd23b8a3ead33e1b49e67f4da37435c6081623bcd059d9ab332d77e3cd0ff8","observation_id":"5d161649-77a9-4bc8-86d2-ef4d6ed28a7a","resolution":{"observed_at":"2026-08-01T21:19:37.294367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.01266","last_updated":"2023-07-17T20:48:11Z","snapshot_observed_at":"2026-08-16T15:43:06.233731Z","submitted_at":"2023-04-03T18:01:01Z","title":"Fast Point Cloud Generation with Diffusion Models in High Energy Physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.01266","snapshot_observed_at":"2026-08-01T21:19:37.355855Z","title":"Mikuni, B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.355855Z"},"links":{"cited_paper":"/paper/2304.01266","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:47ae4d8851896914c770919123fa73edc28304b65eb705dc3368a27c1e9de2fa","observation_id":"26c5c635-0970-491c-9ce4-f5186f38dffe","resolution":{"observed_at":"2026-08-01T21:19:37.355855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.06836","last_updated":"2023-08-18T16:33:39Z","snapshot_observed_at":"2026-08-18T04:16:09.821877Z","submitted_at":"2023-07-13T15:56:23Z","title":"PC-Droid: Faster diffusion and improved quality for particle cloud generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.06836","snapshot_observed_at":"2026-08-01T21:19:37.417639Z","title":"Leigh, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.417639Z"},"links":{"cited_paper":"/paper/2307.06836","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:5a8ded7d3a95fa1929421794965ee28fef80c2c217c6ab0ebdc872bf8405889b","observation_id":"596f81e7-b825-4383-82de-3f0f935f19de","resolution":{"observed_at":"2026-08-01T21:19:37.417639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10475","last_updated":"2024-11-19T16:25:32Z","snapshot_observed_at":"2026-08-16T15:32:14.978532Z","submitted_at":"2023-05-17T18:00:00Z","title":"Jet Diffusion versus JetGPT -- Modern Networks for the LHC","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10475","snapshot_observed_at":"2026-08-01T21:19:37.492486Z","title":"Butter, N","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.492486Z"},"links":{"cited_paper":"/paper/2305.10475","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:b17bfb869207de7bf60e05dd0969a63e986bbe94062fe253303da90b72d03009","observation_id":"5c2a1d9d-dceb-431b-9a4b-f481c7865726","resolution":{"observed_at":"2026-08-01T21:19:37.492486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00049","last_updated":"2023-09-29T18:00:03Z","snapshot_observed_at":"2026-08-18T09:59:20.642229Z","submitted_at":"2023-09-29T18:00:03Z","title":"EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00049","snapshot_observed_at":"2026-08-01T21:19:37.556586Z","title":"Buhmann, C","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.556586Z"},"links":{"cited_paper":"/paper/2310.00049","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:546b6df9f8b24f693d29a720ac63356d9536ee55bbfa0368bacf7301e37822e3","observation_id":"49e08b34-0830-4b83-bf87-a2bee2724183","resolution":{"observed_at":"2026-08-01T21:19:37.556586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09629","last_updated":"2024-12-28T07:23:14Z","snapshot_observed_at":"2026-08-16T13:52:23.026107Z","submitted_at":"2024-05-15T18:00:36Z","title":"CaloDREAM -- Detector Response Emulation via Attentive flow Matching","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09629","snapshot_observed_at":"2026-08-01T21:19:37.684877Z","title":"Favaro, A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.684877Z"},"links":{"cited_paper":"/paper/2405.09629","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:67ef9f5d950f51eb67b854a51b83a385dbd10389882f50563a1af90c4640b1db","observation_id":"5c6abe59-3181-4b15-92e2-be3b44cb29c4","resolution":{"observed_at":"2026-08-01T21:19:37.684877Z","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":"2026-08-01T21:19:37.844621Z","title":"Vaselli, F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.844621Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:8010dd7aaec97b7cd123701709464cd39a632905530af16d24df94f39815b7e1","observation_id":"62d8116c-40f5-4f6d-a111-725becdf3c11","resolution":{"observed_at":"2026-08-01T21:19:37.844621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.02415","last_updated":"2026-04-28T18:09:33Z","snapshot_observed_at":"2026-08-21T06:40:08.257925Z","submitted_at":"2026-04-02T18:00:01Z","title":"Generative models on phase space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.02415","snapshot_observed_at":"2026-08-01T21:19:37.930502Z","title":"Bogorad, I","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:37.930502Z"},"links":{"cited_paper":"/paper/2604.02415","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:0db19a878f573390e2ad40dfa501151ff02a7b5d260dd2a5663be3a4ef8c512c","observation_id":"606df074-c5b0-46c1-b99c-1abc4c488a93","resolution":{"observed_at":"2026-08-01T21:19:37.930502Z","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":"2026-08-01T21:19:38.081181Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.081181Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:ff127d6d6eace924a75eb13b4ba47608ac0f04a16a1d90daf294d970a9f053f0","observation_id":"6d636d30-39a4-4f70-b52a-08addac5133a","resolution":{"observed_at":"2026-08-01T21:19:38.081181Z","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":"2026-08-01T21:19:38.185230Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.185230Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:4430763d6875a6c41645b164954816b79f2e8b299eb60368b64b62aa74e39347","observation_id":"fe0e70e8-0036-4380-8b04-9a8afd8c8b52","resolution":{"observed_at":"2026-08-01T21:19:38.185230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04253","last_updated":"2021-08-09T18:00:03Z","snapshot_observed_at":"2026-08-16T18:04:17.663746Z","submitted_at":"2021-08-09T18:00:03Z","title":"Symmetries, Safety, and Self-Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.04253","snapshot_observed_at":"2026-08-01T21:19:38.337136Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.337136Z"},"links":{"cited_paper":"/paper/2108.04253","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:64749af6a10bc286f960acf1ea804161d4a78c0a7eac058efb014e37ca547bb3","observation_id":"a3051bb5-643e-4f55-8352-6544f8504c40","resolution":{"observed_at":"2026-08-01T21:19:38.337136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.13537","last_updated":"2024-07-11T11:55:25Z","snapshot_observed_at":"2026-08-16T14:24:50.144278Z","submitted_at":"2024-01-24T15:46:32Z","title":"Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.13537","snapshot_observed_at":"2026-08-01T21:19:38.473551Z","title":"Golling, L","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.473551Z"},"links":{"cited_paper":"/paper/2401.13537","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:5e5f9217d25450f748e513839bc05bacfe62ff7dcf03ff2c0950b99ae3e2e443","observation_id":"f343c461-6d6f-4c84-b6c9-edce7867b70a","resolution":{"observed_at":"2026-08-01T21:19:38.473551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05618","last_updated":"2024-09-07T11:17:18Z","snapshot_observed_at":"2026-08-16T14:11:31.180390Z","submitted_at":"2024-03-08T19:00:01Z","title":"OmniJet-$\\alpha$: The first cross-task foundation model for particle physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05618","snapshot_observed_at":"2026-08-01T21:19:38.590438Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.590438Z"},"links":{"cited_paper":"/paper/2403.05618","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:12e3996b1db3f66154261a9abb8dca12807aff088bedfc4fca02d7c1ff374955","observation_id":"59ba03ef-2007-4af6-8383-3794d6271a43","resolution":{"observed_at":"2026-08-01T21:19:38.590438Z","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":"2026-08-01T21:19:38.721441Z","title":"Mikuni and B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.721441Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:43b92f34e9632dc3c09064bd263c3392accd88c5814599388438128e173a029b","observation_id":"9717fec2-4764-4e2b-8182-787e9f0fa8b8","resolution":{"observed_at":"2026-08-01T21:19:38.721441Z","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":"2026-08-01T21:19:38.789215Z","title":"Bhimji, C","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.789215Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:3567db48ab32cb6ab4d0513b58155e054fc2acf7142d4a9f97fac33b97b53950","observation_id":"ecd120af-1aa9-44b9-a1f1-8ede5a2db5f1","resolution":{"observed_at":"2026-08-01T21:19:38.789215Z","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":"2026-08-01T21:19:38.926501Z","title":"Hsuet al., EveNet: A Foundation Model for Par- ticle Collision Data Analysis (2026), arXiv:2601.17126 [hep-ex]","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:38.926501Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:b16542ddf31894eb3eb83198704677c3984c9416876118106acb04e6f5ac126d","observation_id":"fc615026-25de-4338-ab05-5fbc334324f9","resolution":{"observed_at":"2026-08-01T21:19:38.926501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10239","last_updated":"2024-02-14T18:24:49Z","snapshot_observed_at":"2026-08-16T14:18:32.919214Z","submitted_at":"2024-02-14T18:24:49Z","title":"A Language Model for Particle Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10239","snapshot_observed_at":"2026-08-01T21:19:39.035179Z","title":"Huang, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.035179Z"},"links":{"cited_paper":"/paper/2402.10239","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e224b77c6a0435da89a80186562dac804086b687cb848464586617ccd8ce5ca1","observation_id":"0f2909f8-5a58-4642-81ad-699bc88e7c71","resolution":{"observed_at":"2026-08-01T21:19:39.035179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14806","last_updated":"2024-10-30T15:50:21Z","snapshot_observed_at":"2026-08-18T20:53:59.622400Z","submitted_at":"2024-05-23T17:15:41Z","title":"Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14806","snapshot_observed_at":"2026-08-01T21:19:39.138496Z","title":"Spinner, V","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.138496Z"},"links":{"cited_paper":"/paper/2405.14806","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:c633802ce12573b6896235828ab9d5c4a2778466a8cb09dafc50dc70e6b53bc2","observation_id":"c25dce5f-b814-4dc8-9c6f-8ea68bfa364f","resolution":{"observed_at":"2026-08-01T21:19:39.138496Z","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":"2026-08-01T21:19:39.264696Z","title":"Amram, L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.264696Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:1700a2aeeac3fd5e829d3762f72c317edbf134a0e28f43c563fab4db81315d2d","observation_id":"823c86c1-bd13-4794-a071-340d896957be","resolution":{"observed_at":"2026-08-01T21:19:39.264696Z","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":"2026-08-01T21:19:39.372724Z","title":"Omana Kuttan, K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.372724Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:325c174f31f32fca9e3a90f73b7ceaa698201796b6e694e0d3e209b9fd16bd92","observation_id":"d3438522-edc3-4a6f-b0b3-5d830380cb0f","resolution":{"observed_at":"2026-08-01T21:19:39.372724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.02949","last_updated":"2017-11-18T19:21:43Z","snapshot_observed_at":"2026-08-16T12:14:41.062169Z","submitted_at":"2017-08-09T18:00:06Z","title":"Classification without labels: Learning from mixed samples in high energy physics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.02949","snapshot_observed_at":"2026-08-01T21:19:39.444831Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.444831Z"},"links":{"cited_paper":"/paper/1708.02949","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:bfe10c49cc4adbf55e98d1e6673a7a6d3e20590ab724f577ac4b95b97fcbd289","observation_id":"7c01d5df-4f5a-4d8f-8ff1-2454765b2605","resolution":{"observed_at":"2026-08-01T21:19:39.444831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.02664","last_updated":"2018-11-19T16:38:48Z","snapshot_observed_at":"2026-08-15T09:57:22.729056Z","submitted_at":"2018-05-07T18:00:02Z","title":"Anomaly Detection for Resonant New Physics with Machine Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.02664","snapshot_observed_at":"2026-08-01T21:19:39.529039Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.529039Z"},"links":{"cited_paper":"/paper/1805.02664","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:b64a6fcc2199b800af0298995d8c97521a357be5a5087273bd4a981be93a8052","observation_id":"90f9a37b-7db5-4262-841c-aed08bc30313","resolution":{"observed_at":"2026-08-01T21:19:39.529039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04990","last_updated":"2020-05-10T04:48:08Z","snapshot_observed_at":"2026-08-13T14:46:52.310601Z","submitted_at":"2020-01-14T19:00:02Z","title":"Anomaly Detection with Density Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04990","snapshot_observed_at":"2026-08-01T21:19:39.585647Z","title":"Nachman and D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.585647Z"},"links":{"cited_paper":"/paper/2001.04990","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:87170746ffdc7dada6acb83a2e0741c682803a1f99c0c1667448e575c9ab0b5d","observation_id":"fab469af-3855-49b2-990e-176d5e4ed715","resolution":{"observed_at":"2026-08-01T21:19:39.585647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00546","last_updated":"2022-09-11T19:04:53Z","snapshot_observed_at":"2026-08-20T20:31:44.677920Z","submitted_at":"2021-09-01T18:00:00Z","title":"Classifying Anomalies THrough Outer Density Estimation (CATHODE)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00546","snapshot_observed_at":"2026-08-01T21:19:39.662913Z","title":"Hallin, J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.662913Z"},"links":{"cited_paper":"/paper/2109.00546","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:17e9c51f48b3c52a75edd9d1a8ade68821b9f01a70cb4fd820ee0e5ffef7a028","observation_id":"0f9b16f9-6d1b-4530-b2c4-20ef90e2e300","resolution":{"observed_at":"2026-08-01T21:19:39.662913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.09470","last_updated":"2023-02-10T12:58:15Z","snapshot_observed_at":"2026-08-21T23:30:54.582632Z","submitted_at":"2022-03-17T17:31:24Z","title":"CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.09470","snapshot_observed_at":"2026-08-01T21:19:39.725137Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.725137Z"},"links":{"cited_paper":"/paper/2203.09470","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:bc5694dc4144d8a641f45fe7c38567127d5389e4c47e0477b91791e4f615a319","observation_id":"d6df1475-5da1-40aa-aaa5-48384fbb3a10","resolution":{"observed_at":"2026-08-01T21:19:39.725137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.05001","last_updated":"2020-01-14T19:00:09Z","snapshot_observed_at":"2026-08-16T12:16:31.584866Z","submitted_at":"2020-01-14T19:00:09Z","title":"Simulation Assisted Likelihood-free Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.05001","snapshot_observed_at":"2026-08-01T21:19:39.776755Z","title":"Andreassen, B","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.776755Z"},"links":{"cited_paper":"/paper/2001.05001","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:16692e70c8f5b7ec09d849410909cd057ca4fedc5203e54108895d9236e89d48","observation_id":"1f36f9e3-7179-472a-814e-58877550f9a4","resolution":{"observed_at":"2026-08-01T21:19:39.776755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.11285","last_updated":"2023-06-14T20:31:37Z","snapshot_observed_at":"2026-08-16T16:06:51.592649Z","submitted_at":"2022-12-21T19:00:00Z","title":"FETA: Flow-Enhanced Transportation for Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.11285","snapshot_observed_at":"2026-08-01T21:19:39.903519Z","title":"Golling, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:39.903519Z"},"links":{"cited_paper":"/paper/2212.11285","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:08a383559bc8e0f43c4c81cf638cc02290a3d9e7a607a31593ae778ad5ddc0a4","observation_id":"087255cc-7275-409a-bbbf-f45ff1a72767","resolution":{"observed_at":"2026-08-01T21:19:39.903519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10130","last_updated":"2023-12-19T14:08:10Z","snapshot_observed_at":"2026-08-16T14:34:22.010970Z","submitted_at":"2023-12-15T15:53:10Z","title":"Improving new physics searches with diffusion models for event observables and jet constituents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10130","snapshot_observed_at":"2026-08-01T21:19:40.013434Z","title":"Sengupta, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.013434Z"},"links":{"cited_paper":"/paper/2312.10130","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:83cd61c8d260aa9ac47aea11226fc16f4ab7593dfc848779526e6ebf8178994e","observation_id":"e049e24e-548e-4e5a-bd10-a5a029eba77c","resolution":{"observed_at":"2026-08-01T21:19:40.013434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.08508","last_updated":"2021-12-15T22:26:28Z","snapshot_observed_at":"2026-08-16T17:48:49.627663Z","submitted_at":"2021-10-16T08:24:29Z","title":"Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.08508","snapshot_observed_at":"2026-08-01T21:19:40.158328Z","title":"Jawahar, T","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.158328Z"},"links":{"cited_paper":"/paper/2110.08508","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:436a257a9a2621c9535d252af55336fd605598ce4f553b9fb56a83d803aba308","observation_id":"01d15ab5-b26a-4fd8-bef0-438411b1b529","resolution":{"observed_at":"2026-08-01T21:19:40.158328Z","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":"2026-08-01T21:19:40.285172Z","title":"Metzger, L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.285172Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:6f0e2b54c0b74b4a15b14f150cfefca39e40f6eea7f5f1278674454bea1b620a","observation_id":"71d3d011-6521-41ad-90c3-aa56adae7212","resolution":{"observed_at":"2026-08-01T21:19:40.285172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.08320","last_updated":"2021-01-20T21:03:06Z","snapshot_observed_at":"2026-08-16T18:51:04.616922Z","submitted_at":"2021-01-20T21:03:06Z","title":"The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.08320","snapshot_observed_at":"2026-08-01T21:19:40.371434Z","title":"Kasieczkaet al., The LHC Olympics 2020 a com- munity challenge for anomaly detection in high en- ergy physics, Rept","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.371434Z"},"links":{"cited_paper":"/paper/2101.08320","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:9a683039a6479615fd54e3512f4708c700acc555bbeacdc7382234611ec9e7d7","observation_id":"8e96ce9e-e2a9-49b4-951f-b5760a34ad8e","resolution":{"observed_at":"2026-08-01T21:19:40.371434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.03769","last_updated":"2021-12-07T15:26:42Z","snapshot_observed_at":"2026-08-16T17:36:26.434460Z","submitted_at":"2021-12-07T15:26:42Z","title":"Machine Learning in the Search for New Fundamental Physics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.03769","snapshot_observed_at":"2026-08-01T21:19:40.459588Z","title":"Karagiorgi, G","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.459588Z"},"links":{"cited_paper":"/paper/2112.03769","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:a407be57ac1469ea2af88058c64d048e47625ec45330a238cb67dc2fb641e365","observation_id":"b8b4a62e-d40b-48dc-8745-14b246a5d444","resolution":{"observed_at":"2026-08-01T21:19:40.459588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14190","last_updated":"2023-12-20T12:40:00Z","snapshot_observed_at":"2026-08-19T17:56:22.997909Z","submitted_at":"2023-12-20T12:40:00Z","title":"Machine Learning for Anomaly Detection in Particle Physics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.14190","snapshot_observed_at":"2026-08-01T21:19:40.533233Z","title":"Belis, P","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.533233Z"},"links":{"cited_paper":"/paper/2312.14190","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:4f9c363a2e38b30582903077e9c6692f05cc0a0523385c309f433baa2bd48762","observation_id":"eb5a2def-25db-4b36-ab6b-7b2eaa407337","resolution":{"observed_at":"2026-08-01T21:19:40.533233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02983","last_updated":"2022-01-11T16:58:05Z","snapshot_observed_at":"2026-08-07T02:54:42.508842Z","submitted_at":"2020-05-06T17:36:56Z","title":"Dijet resonance search with weak supervision using $\\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02983","snapshot_observed_at":"2026-08-01T21:19:40.618293Z","title":"Aadet al.(ATLAS), Dijet resonance search with weak supervision using √s= 13 TeVppcollisions in the ATLAS detector, Phys","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.618293Z"},"links":{"cited_paper":"/paper/2005.02983","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:6a050c0d07ddef7aa12189c1fd3f42d5e5efdb98015f7ea70c461562e73829e0","observation_id":"511ea3ea-a032-4cdf-bb48-c571a533ac98","resolution":{"observed_at":"2026-08-01T21:19:40.618293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01612","last_updated":"2024-02-21T09:56:48Z","snapshot_observed_at":"2026-08-16T15:18:53.130613Z","submitted_at":"2023-07-04T09:54:21Z","title":"Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\\sqrt{s} = 13$ TeV with the ATLAS detector","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01612","snapshot_observed_at":"2026-08-01T21:19:40.694500Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.694500Z"},"links":{"cited_paper":"/paper/2307.01612","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:7ec11e17780b91451735c2c7bfaea822a1ff35b82e88c85cc6587cad21914a37","observation_id":"f5bdc7b6-fa8b-45c6-9010-8b58c5398bb0","resolution":{"observed_at":"2026-08-01T21:19:40.694500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03747","last_updated":"2025-06-03T17:14:54Z","snapshot_observed_at":"2026-08-13T05:13:52.471021Z","submitted_at":"2024-12-04T22:34:03Z","title":"Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\\sqrt{s}$ = 13 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03747","snapshot_observed_at":"2026-08-01T21:19:40.762407Z","title":"Chekhovskyet al.(CMS), Model-agnostic search for dijet resonances with anomalous jet substructure in pro- ton–proton collisions at √s= 13 TeV, Rept","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.762407Z"},"links":{"cited_paper":"/paper/2412.03747","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:4d09a4bd148db15586a2eca10bcb7a2546cbe0e45caffacbf5b05ad749fe4595","observation_id":"9b46f994-3dca-4a9d-a030-87ea9c4bea4a","resolution":{"observed_at":"2026-08-01T21:19:40.762407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03660","last_updated":"2024-02-26T17:52:00Z","snapshot_observed_at":"2026-08-16T15:57:01.124521Z","submitted_at":"2023-02-07T18:21:24Z","title":"Flow Matching on General Geometries","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03660","snapshot_observed_at":"2026-08-01T21:19:40.823242Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.823242Z"},"links":{"cited_paper":"/paper/2302.03660","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e4548558a23da15ac2de67a9e4dc63af8fb9a326b49ff17e60cfe38c13651df6","observation_id":"449895dd-91c5-4661-80d0-6b9c9f46115b","resolution":{"observed_at":"2026-08-01T21:19:40.823242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-08-19T04:42:40.974785Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-01T21:19:40.884553Z","title":"Vaswani, N","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.884553Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:fc98a9a2a2301c1ef5c8024ce745e6a4caf10256b871a1f6a2c38707e7ce0fbb","observation_id":"1602baa1-dd0a-43f0-bb56-e6d0df5cfb2d","resolution":{"observed_at":"2026-08-01T21:19:40.884553Z","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":"2026-08-01T21:19:40.970576Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:40.970576Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:10deff8acf817114c75a23c2be30971025e1be3c07ef6deb51d7376b20f09681","observation_id":"0a9ef6e7-2675-4bac-b76c-20d53269f91d","resolution":{"observed_at":"2026-08-01T21:19:40.970576Z","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":"2026-08-01T21:19:41.039402Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.039402Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:f279b7db3071e79cf58df0c64ad8d12c43ba6c81972114170c97b9878ca9d12b","observation_id":"c394efcd-cceb-4cd9-a534-e57c66a19458","resolution":{"observed_at":"2026-08-01T21:19:41.039402Z","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":"2026-08-01T21:19:41.097269Z","title":"Navaset al.(Particle Data Group), Review of particle physics, Phys","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.097269Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:f54ec8ba9cbc53c5c595dd9b3ca1a485e83d0eaf7fc602cbe5d1b844db199ac6","observation_id":"17f9ccc0-b964-4fb4-94f7-e3fb7c5b6e09","resolution":{"observed_at":"2026-08-01T21:19:41.097269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1104.3038","last_updated":"2011-07-25T12:17:23Z","snapshot_observed_at":"2026-08-20T07:10:18.226422Z","submitted_at":"2011-04-15T12:47:30Z","title":"Measurement of the differential cross-sections of inclusive, prompt and non-prompt J/psi production in proton-proton collisions at sqrt(s) = 7 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1104.3038","snapshot_observed_at":"2026-08-01T21:19:41.160300Z","title":"Aadet al.(ATLAS), Measurement of the differential cross-sections of inclusive, prompt and non-promptJ/ψ production in proton-proton collisions at √s= 7 TeV, Nucl","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.160300Z"},"links":{"cited_paper":"/paper/1104.3038","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:acc0b3f85eb9adcdc6052617196d9a044d5c066c9d981c53990000a49eb9ce57","observation_id":"fbd63c21-c189-4291-b364-0f420c6ae45a","resolution":{"observed_at":"2026-08-01T21:19:41.160300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1211.7255","last_updated":"2013-03-22T14:32:37Z","snapshot_observed_at":"2026-08-15T00:37:34.578356Z","submitted_at":"2012-11-30T13:54:28Z","title":"Measurement of Upsilon production in 7 TeV pp collisions at ATLAS","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1211.7255","snapshot_observed_at":"2026-08-01T21:19:41.230458Z","title":"Aadet al.(ATLAS), Measurement of upsilon produc- tion in 7 TeVppcollisions at ATLAS, Phys","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.230458Z"},"links":{"cited_paper":"/paper/1211.7255","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:87b4aab268146fe64fae202b0f13429c5ee952c8265ebe3363bb26b01e329836","observation_id":"1c605ff4-1c33-41e5-963d-e53a7fdb7545","resolution":{"observed_at":"2026-08-01T21:19:41.230458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05167","last_updated":"2018-02-09T12:30:12Z","snapshot_observed_at":"2026-08-19T19:29:06.502005Z","submitted_at":"2017-10-14T11:57:19Z","title":"Measurement of the Drell--Yan triple-differential cross section in $pp$ collisions at $\\sqrt{s} = 8$ TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05167","snapshot_observed_at":"2026-08-01T21:19:41.312775Z","title":"Aaboudet al.(ATLAS), Measurement of the Drell- Yan triple-differential cross section inppcollisions at√s= 8 TeV, JHEP12, 059, arXiv:1710.05167 [hep-ex]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.312775Z"},"links":{"cited_paper":"/paper/1710.05167","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e91a615a6c8f80530caca32380c8ff13d3a2b5b82f1c3f0f4e58ddffec6e66ed","observation_id":"318cb526-7010-41f6-a93f-2c5213b8fbf2","resolution":{"observed_at":"2026-08-01T21:19:41.312775Z","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":"2026-08-01T21:19:41.394718Z","title":"Weinberg, A Model of Leptons, Phys","venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.394718Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:c4dfff72eff93d7d27dec13e390d7b085a3d4361369a2c1c50af632ea4cf339b","observation_id":"66a370c7-8929-4687-9593-00b763ed131b","resolution":{"observed_at":"2026-08-01T21:19:41.394718Z","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":"2026-08-01T21:19:41.517526Z","title":null,"venue":null,"work_id":null,"year":1942},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.517526Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:32f13957ea008b53f533e511a9ef1a776905d9df4e3ba342efee0b5b1760cae0","observation_id":"065deadf-1686-43fb-8071-e85441327cca","resolution":{"observed_at":"2026-08-01T21:19:41.517526Z","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":"2026-08-01T21:19:41.659975Z","title":null,"venue":null,"work_id":null,"year":1969},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.659975Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:8d42ddfe1b156f44301dbc321fa5deb664158618d4def452d0e5f3c1bed86c35","observation_id":"cf60ea55-bd06-4fb3-a647-3bb48fccb3c1","resolution":{"observed_at":"2026-08-01T21:19:41.659975Z","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":"2026-08-01T21:19:41.774767Z","title":"Kullback and R","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.774767Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:03854712a0504231a67b75a3ef9d58f269a0ff8db967bd72022b325745758ff2","observation_id":"61305696-dac5-4a58-8832-ac589e7ad518","resolution":{"observed_at":"2026-08-01T21:19:41.774767Z","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":"2026-08-01T21:19:41.871718Z","title":"Jeffreys,Theory of Probability, 2nd ed","venue":null,"work_id":null,"year":1948},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:41.871718Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:68a8f74db6b6507717a8825c57e5339e1ad624664bfc5a607787bec82588f83f","observation_id":"f718e69f-6e6a-477d-b107-ebc92517f4aa","resolution":{"observed_at":"2026-08-01T21:19:41.871718Z","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":"2026-08-01T21:19:42.018875Z","title":null,"venue":null,"work_id":null,"year":1957},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.018875Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:13f00bd138a3546a821c3da9f584de7f6791cbaf223520e09b509d7f4350f662","observation_id":"3350b2d7-eadf-47e3-b710-95dc016bbf99","resolution":{"observed_at":"2026-08-01T21:19:42.018875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10295","last_updated":"2023-04-21T16:14:36Z","snapshot_observed_at":"2026-08-16T16:15:04.350032Z","submitted_at":"2022-11-18T15:36:28Z","title":"Evaluating generative models in high energy physics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10295","snapshot_observed_at":"2026-08-01T21:19:42.117939Z","title":"Kansal, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.117939Z"},"links":{"cited_paper":"/paper/2211.10295","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:3b46f773b2ff541cacc9836a9672466fa56496e56103b368ac582bb00875606a","observation_id":"7df40bac-ad6d-4519-83b0-a116fc9f7e1d","resolution":{"observed_at":"2026-08-01T21:19:42.117939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16774","last_updated":"2023-12-07T19:05:52Z","snapshot_observed_at":"2026-08-21T18:16:59.900226Z","submitted_at":"2023-05-26T09:35:16Z","title":"How to Understand Limitations of Generative Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16774","snapshot_observed_at":"2026-08-01T21:19:42.242158Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.242158Z"},"links":{"cited_paper":"/paper/2305.16774","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:b8df5eae9704a54e0dfc77cc1cdaa0bea99fd0c7ea8aadfd00b3d25d0a1cde98","observation_id":"60bada0c-9840-4419-9bb4-e458420990e4","resolution":{"observed_at":"2026-08-01T21:19:42.242158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16336","last_updated":"2024-09-24T13:58:46Z","snapshot_observed_at":"2026-08-20T03:20:09.847238Z","submitted_at":"2024-09-24T13:58:46Z","title":"Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.16336","snapshot_observed_at":"2026-08-01T21:19:42.353246Z","title":"Grossi, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.353246Z"},"links":{"cited_paper":"/paper/2409.16336","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:b6c5a990c0e58febe5d15ccd5e48c283e616e058af58df659a10650e5ba03a28","observation_id":"899eef51-0861-4732-8ba2-062f305db405","resolution":{"observed_at":"2026-08-01T21:19:42.353246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1407.5063","last_updated":"2014-11-13T10:33:38Z","snapshot_observed_at":"2026-08-18T10:04:06.159106Z","submitted_at":"2014-07-18T17:25:56Z","title":"Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1407.5063","snapshot_observed_at":"2026-08-01T21:19:42.464190Z","title":"Aadet al.(ATLAS), Electron and photon energy cali- bration with the ATLAS detector using LHC Run 1 data, Eur","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.464190Z"},"links":{"cited_paper":"/paper/1407.5063","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:f0cb2f335c981500afdcd3e6907bd821cf41cf633276722f7641b384d9557a53","observation_id":"013a2e99-a483-4590-b084-0fd3a92f61bb","resolution":{"observed_at":"2026-08-01T21:19:42.464190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.05598","last_updated":"2016-05-30T13:21:21Z","snapshot_observed_at":"2026-08-18T19:18:10.342483Z","submitted_at":"2016-03-17T18:06:45Z","title":"Muon reconstruction performance of the ATLAS detector in proton--proton collision data at $\\sqrt{s}$=13 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.05598","snapshot_observed_at":"2026-08-01T21:19:42.583508Z","title":"Aadet al.(ATLAS), Muon reconstruction perfor- mance of the ATLAS detector in proton–proton collision data at √s=13 TeV, Eur","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.583508Z"},"links":{"cited_paper":"/paper/1603.05598","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:382238344eb00b875af12d95aeba87d54677a4380ec910d559d9f09b2a6bf883","observation_id":"4d1eda19-d725-4200-b8c2-ef71a4ad698c","resolution":{"observed_at":"2026-08-01T21:19:42.583508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05858","last_updated":"2025-06-24T08:08:12Z","snapshot_observed_at":"2026-08-21T03:51:31.563192Z","submitted_at":"2024-02-08T17:42:34Z","title":"The performance of missing transverse momentum reconstruction and its significance with the ATLAS detector using 140 fb$^{-1}$ of $\\sqrt{s}=13$ TeV $pp$ collisions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05858","snapshot_observed_at":"2026-08-01T21:19:42.705946Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.705946Z"},"links":{"cited_paper":"/paper/2402.05858","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:ed0f0974aa711ec4d895096bcc8466c22f670de7bdb0b119a214e296bca0ee57","observation_id":"08d90c00-465b-40b5-a057-2f788af328a3","resolution":{"observed_at":"2026-08-01T21:19:42.705946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.09624","last_updated":"2017-12-04T13:12:58Z","snapshot_observed_at":"2026-08-14T20:37:25.352069Z","submitted_at":"2017-08-31T09:11:31Z","title":"Search for an invisibly decaying Higgs boson or dark matter candidates produced in association with a $Z$ boson in $pp$ collisions at $\\sqrt{s} =$ 13 TeV with the ATLAS detector","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.09624","snapshot_observed_at":"2026-08-01T21:19:42.810084Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.810084Z"},"links":{"cited_paper":"/paper/1708.09624","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:bc4628d755167505c2846650e70307972a129799b6ac5e67ce3701f5fd3f14d4","observation_id":"ae79653f-7dc4-435f-9584-8b0205f5af04","resolution":{"observed_at":"2026-08-01T21:19:42.810084Z","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":"2026-08-01T21:19:42.915686Z","title":null,"venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:42.915686Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:688817e473757c0c641e083616ea289340c2869c0771acf1b1a3d349e7203e23","observation_id":"bc0a8cff-87f7-4729-9319-5dcaee09240c","resolution":{"observed_at":"2026-08-01T21:19:42.915686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00863","last_updated":"2018-09-04T11:02:37Z","snapshot_observed_at":"2026-08-19T07:58:28.043193Z","submitted_at":"2018-06-03T19:49:04Z","title":"Measurement of the weak mixing angle using the forward-backward asymmetry of Drell-Yan events in pp collisions at 8 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00863","snapshot_observed_at":"2026-08-01T21:19:43.036321Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.036321Z"},"links":{"cited_paper":"/paper/1806.00863","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:96fdf68229a0ea00baafce9a7422e72bb995052ce3de0e43772aacf39ac93bb1","observation_id":"c9db89e6-07e0-4ed4-9c17-4952ae88aa14","resolution":{"observed_at":"2026-08-01T21:19:43.036321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07622","last_updated":"2025-05-21T15:04:27Z","snapshot_observed_at":"2026-08-19T02:36:12.845418Z","submitted_at":"2024-08-14T15:44:43Z","title":"Measurement of the Drell-Yan forward-backward asymmetry and of the effective leptonic weak mixing angle in proton-proton collisions at $\\sqrt{s}$ = 13 TeV","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.07622","snapshot_observed_at":"2026-08-01T21:19:43.150258Z","title":"Hayrapetyanet al.(CMS), Measurement of the Drell–Yan forward-backward asymmetry and of the ef- fective leptonic weak mixing angle in proton-proton col- lisions at s=13TeV, Phys","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.150258Z"},"links":{"cited_paper":"/paper/2408.07622","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:d40cd6294fcb0e445ca37bebd57a75562252cab8bb0e40f00911a87445bd684b","observation_id":"4edf6cd5-554c-4763-92b9-fe0fbaf4e98e","resolution":{"observed_at":"2026-08-01T21:19:43.150258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01772","last_updated":"2019-05-02T08:59:58Z","snapshot_observed_at":"2026-08-14T18:19:59.891475Z","submitted_at":"2018-10-03T14:35:05Z","title":"Measurement of the top quark mass in the $t\\bar{t}\\to$ lepton+jets channel from $\\sqrt{s}=8$ TeV ATLAS data and combination with previous results","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01772","snapshot_observed_at":"2026-08-01T21:19:43.222137Z","title":"Aaboudet al.(ATLAS), Measurement of the top quark mass in thet ¯t→lepton+jets channel from √s= 8 TeV ATLAS data and combination with previous results, Eur","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.222137Z"},"links":{"cited_paper":"/paper/1810.01772","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:89cc1213cde376ed5aef0f9f75dd2e4e6ae51cdf85429473f5e8572957beae68","observation_id":"ea97630e-f40b-4210-9533-dca4abbe18cf","resolution":{"observed_at":"2026-08-01T21:19:43.222137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5595","last_updated":"2014-03-12T12:47:58Z","snapshot_observed_at":"2026-08-17T19:26:31.600539Z","submitted_at":"2013-12-19T15:41:55Z","title":"A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5595","snapshot_observed_at":"2026-08-01T21:19:43.312482Z","title":"Erdmann, S","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.312482Z"},"links":{"cited_paper":"/paper/1312.5595","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:597290599edb24a3870fb8f68fc60a595a529c8e8ae2345abdc9b626c4e20cac","observation_id":"7913788f-959b-470a-bdb8-29da230de8ea","resolution":{"observed_at":"2026-08-01T21:19:43.312482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1407.6583","last_updated":"2014-09-08T14:36:08Z","snapshot_observed_at":"2026-08-18T07:12:20.476677Z","submitted_at":"2014-07-24T13:54:21Z","title":"Search for Scalar Diphoton Resonances in the Mass Range $65-600$ GeV with the ATLAS Detector in $pp$ Collision Data at $\\sqrt{s}$ = 8 $TeV$","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1407.6583","snapshot_observed_at":"2026-08-01T21:19:43.439769Z","title":"Aadet al.(ATLAS), Search for Scalar Diphoton Reso- nances in the Mass Range 65−600 GeV with the ATLAS Detector inppCollision Data at √s= 8T eV, Phys","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.439769Z"},"links":{"cited_paper":"/paper/1407.6583","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:e60bc94a6abb09994006c58f2e8be56d8b76d3b5b305fb7e9371ce78fb72edb5","observation_id":"00c277bd-7472-4826-ae47-ccb0f76a3cd3","resolution":{"observed_at":"2026-08-01T21:19:43.439769Z","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":"2026-08-01T21:19:43.547585Z","title":"Skwarnicki,A study of the radiative cascade transi- tions between the Upsilon-prime and Upsilon resonances, Ph.D","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.547585Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:f62737ace3e3981100d5026edf6108ccf38531b3ff482a3968e3eda4b297a803","observation_id":"0b8c87a5-494a-4e15-8124-4a0949450a8b","resolution":{"observed_at":"2026-08-01T21:19:43.547585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.03447","last_updated":"2021-05-12T13:37:45Z","snapshot_observed_at":"2026-08-14T09:21:59.674500Z","submitted_at":"2020-04-07T14:53:04Z","title":"Higgs boson production cross-section measurements and their EFT interpretation in the $4\\ell$ decay channel at $\\sqrt{s}$ = 13 TeV with the ATLAS detector","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.03447","snapshot_observed_at":"2026-08-01T21:19:43.667235Z","title":"Aadet al.(ATLAS), Higgs boson production cross- section measurements and their EFT interpretation in the 4ℓdecay channel at √s=13 TeV with the AT- LAS detector, Eur","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.667235Z"},"links":{"cited_paper":"/paper/2004.03447","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:461438ffe9089c2bb46bc8c8e0895884ef394d36b30b8675c83af65a84b61eee","observation_id":"9be9f9a2-d90a-4fe4-9581-8880efe9d0e3","resolution":{"observed_at":"2026-08-01T21:19:43.667235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-16T02:30:42.660030Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-01T21:19:43.770995Z","title":"Lipman, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.770995Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:def94209b056d93d2a53be70218ef329cb397d8ec0262d4b7e92e79756c372a3","observation_id":"088c90e0-212d-431d-86d2-e372e57f0d6b","resolution":{"observed_at":"2026-08-01T21:19:43.770995Z","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":"2026-08-01T21:19:43.878674Z","title":"Shoemake, Animating Rotation with Quaternion Curves, inProc","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.878674Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:7011c4ae74770d4f1e1ac718e27df3e085a05dfac47ff8579282251670f863cd","observation_id":"725fb3c9-7bfc-4a2f-9a3b-2c3169086db3","resolution":{"observed_at":"2026-08-01T21:19:43.878674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09748","last_updated":"2023-03-02T09:06:55Z","snapshot_observed_at":"2026-08-17T19:22:44.145403Z","submitted_at":"2022-12-19T18:59:58Z","title":"Scalable Diffusion Models with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09748","snapshot_observed_at":"2026-08-01T21:19:43.988369Z","title":"Peebles and S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:43.988369Z"},"links":{"cited_paper":"/paper/2212.09748","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:32e8e602f9b8e7b632b486754f0b2cd5450b101df32f3a34d1bcf5a00c75b1e8","observation_id":"6b9971ff-fd85-4931-b0d5-c5ba00f333f0","resolution":{"observed_at":"2026-08-01T21:19:43.988369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.07467","last_updated":"2019-10-16T16:44:22Z","snapshot_observed_at":"2026-08-08T08:22:25.110228Z","submitted_at":"2019-10-16T16:44:22Z","title":"Root Mean Square Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.07467","snapshot_observed_at":"2026-08-01T21:19:44.143881Z","title":"Zhang and R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:44.143881Z"},"links":{"cited_paper":"/paper/1910.07467","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:8d10bb9815cbae2ae977f2ee336509a423390f4b5557891fb649efedba12ec34","observation_id":"98321406-5be5-4f5a-947b-1feb5238f84e","resolution":{"observed_at":"2026-08-01T21:19:44.143881Z","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":"2026-08-01T21:19:44.257628Z","title":null,"venue":null,"work_id":null,"year":1964},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:44.257628Z"},"links":{"citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:3c4c0da7f90d86dba9934ea10ec4524a71f062d8d0784682f749ade71153c880","observation_id":"78f1938a-d06f-4806-8eb3-37fa049287b9","resolution":{"observed_at":"2026-08-01T21:19:44.257628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-01T21:19:44.351958Z","title":"Loshchilov and F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:44.351958Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:da5c16bd2542e6630ec638afdfb633ce8c3fb0dc1fc1ada66ed457f70a5c7eee","observation_id":"83a64547-fc59-4ff6-b69e-5303d40a5b55","resolution":{"observed_at":"2026-08-01T21:19:44.351958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06335","last_updated":"2025-07-16T14:15:01Z","snapshot_observed_at":"2026-08-17T21:31:23.198162Z","submitted_at":"2024-04-09T14:19:45Z","title":"Software and computing for Run 3 of the ATLAS experiment at the LHC","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.06335","snapshot_observed_at":"2026-08-01T21:19:44.409630Z","title":"Aadet al.(ATLAS), Software and computing for Run 3 of the ATLAS experiment at the LHC, Eur","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:44.409630Z"},"links":{"cited_paper":"/paper/2404.06335","citing_paper":"/paper/2607.16144"},"observation_digest":"sha256:57224775b7784f011e53c221ab3b72943107a3e5d8ccd9353490a0d602720d69","observation_id":"82695d9f-13fc-4906-9fcd-7a7f8a840947","resolution":{"observed_at":"2026-08-01T21:19:44.409630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.16144","last_updated":"2026-07-17T17:23:22Z","latest_version":1,"primary_category":"hep-ph","snapshot_observed_at":"2026-08-13T07:46:36.496344Z","submitted_at":"2026-07-17T17:23:22Z","title":"Learning Standard Model structure from LHC data with Riemannian flow matching"},"reference_resolution":{"displayed":93,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":93,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":93},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 0 inbound Pith citation observations for arXiv:2607.16144."}