{"as_of":"2026-08-14T13:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:51d2abfbee9249537c2fd40bf49d9992b8ed00bb2fbcf9052f2265f4b33a1bbc","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:28:43.721439Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2507.05589/citation-record","integrity":"/paper/2507.05589/integrity","json":"/paper/2507.05589/citation-record.json","paper":"/paper/2507.05589"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:36.562672Z","title":"K., Ag¨ ueros, M","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:36.562672Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:0bc7f21b3dafad0adae8da4f8f369440824e8dd79b88d4d3d0ab86fe704b0ee1","observation_id":"a90c5d08-475a-45f4-b0b2-2231cf06146d","resolution":{"observed_at":"2026-08-06T19:28:36.562672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00027","last_updated":"2020-10-30T18:13:32Z","snapshot_observed_at":"2026-08-09T15:19:21.073628Z","submitted_at":"2020-10-30T18:13:32Z","title":"The power of quantum neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.00027","snapshot_observed_at":"2026-08-06T19:28:36.695173Z","title":"2020, arXiv e-prints, arXiv:2011.00027, doi: 10.48550/arXiv.2011.00027","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:36.695173Z"},"links":{"cited_paper":"/paper/2011.00027","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:95b51745a8164ca414f823c9022910c7bc9604c39c4b82f2ad8279f8129cb762","observation_id":"dfad8d4b-e2bf-4090-bb7a-72df9b3a1a31","resolution":{"observed_at":"2026-08-06T19:28:36.695173Z","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":"2026-08-06T19:28:36.861887Z","title":"2020, ApJ, 896, 39, doi: 10.3847/1538-4357/ab91ba","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:36.861887Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:c0215898ff624387d83d25b77b004655258158a73ef60a2f5a2dc435fe5a97e6","observation_id":"b22e0a27-9529-4ad8-a60e-2e36edd575c4","resolution":{"observed_at":"2026-08-06T19:28:36.861887Z","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":"2026-08-06T19:28:37.012584Z","title":"K., & Thorat, K","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.012584Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:a73d213ca7fb819283f6a995faa0c792b7fd26b64ad51bb1f3e358232751279c","observation_id":"8999a5b7-e561-45d6-b8e4-46dd79fe5db3","resolution":{"observed_at":"2026-08-06T19:28:37.012584Z","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":"2026-08-06T19:28:37.112219Z","title":"2021, Nature, 592, 704, doi: 10.1038/s41586-021-03394-6 Astropy Collaboration, Robitaille, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.112219Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:365830323f01c3a0867b697604895cdd04fcb24b7281a68b57bd31de491511f2","observation_id":"e7f4462d-bf53-4b72-9465-3058066d7811","resolution":{"observed_at":"2026-08-06T19:28:37.112219Z","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":"2026-08-06T19:28:37.207929Z","title":"2022, MNRAS, 509, 3504, doi: 10.1093/mnras/stab3259","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.207929Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:318cc8d15739d2b0f6239c50341b6eca28d57911dde42db716fe356b0b2be14a","observation_id":"3a66a6a2-fc2c-4a29-a632-645980b34ed3","resolution":{"observed_at":"2026-08-06T19:28:37.207929Z","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":"2026-08-06T19:28:37.335710Z","title":"H., White, R","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.335710Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:207616b02a940acb968e519cac13775ef5279ad2db39134d63c6eb8a6d4f6314","observation_id":"7341b95f-5742-4df4-b2c1-615899f2fe1f","resolution":{"observed_at":"2026-08-06T19:28:37.335710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.10872","last_updated":"2020-01-29T14:48:51Z","snapshot_observed_at":"2026-08-13T18:50:38.315038Z","submitted_at":"2020-01-29T14:48:51Z","title":"A scale-dependent notion of effective dimension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.10872","snapshot_observed_at":"2026-08-06T19:28:37.492186Z","title":"2020, arXiv e-prints, arXiv:2001.10872, doi: 10.48550/arXiv.2001.10872","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.492186Z"},"links":{"cited_paper":"/paper/2001.10872","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:cced671099463f179300f6cad09af85db73b4ffbf379aa043c633f13b373722c","observation_id":"b5374c28-e7eb-43fc-8da4-cccc2696cad4","resolution":{"observed_at":"2026-08-06T19:28:37.492186Z","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":"2026-08-06T19:28:37.624775Z","title":"2014, Advances in Space Research, 53, 900, doi: 10.1016/j.asr.2013.07.045","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.624775Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7ecc8a71355ee33c62377c1f6725cdccac48bd235d11aab9d91e37ab0019c266","observation_id":"857f9d1f-54f1-4d29-84ec-36f633b982e0","resolution":{"observed_at":"2026-08-06T19:28:37.624775Z","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":"2026-08-06T19:28:37.714930Z","title":"J., Geim, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.714930Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:a7310f5f5f1ae4c973bc03a5e1a9a3953f3c878a32f0bf317a4b4ae691927100","observation_id":"410edb45-6907-4aae-bff1-045cb4d9981e","resolution":{"observed_at":"2026-08-06T19:28:37.714930Z","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":"2026-08-06T19:28:37.878425Z","title":"J., Tr¨ umper, J., et al","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:37.878425Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:e527a9ec91caffed7b00e2c407c51f0839e3076f76f1dc5c848a5c8105b86d79","observation_id":"186f66e8-aaab-466c-8042-81befe61683c","resolution":{"observed_at":"2026-08-06T19:28:37.878425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11047","last_updated":"2025-03-03T16:01:45Z","snapshot_observed_at":"2026-08-12T23:00:30.723276Z","submitted_at":"2024-08-20T17:55:25Z","title":"Quantum machine learning algorithms for anomaly detection: A review","version":3},"cited_work":{"arxiv_id":"2408.11047","doi":"10.48550/arxiv.2408.11047","metadata_source":"pith","pith_arxiv_id":"2408.11047","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Quantum machine learning algorithms for anomaly detection: A review","venue":"quant-ph","work_id":"721438c6-28b4-4ea3-9644-65046c2405f5","year":2024},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.022376Z"},"links":{"cited_paper":"/paper/2408.11047","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:43595d00a2bbe813406c6f57e56ced4fd891c21518b4ebd5a9fe70b3189793f9","observation_id":"1f9200d4-630a-419e-b098-7977a7f12613","resolution":{"observed_at":"2026-08-06T19:28:46.760850Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:38.107865Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.107865Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:6bcfef1921e9549a4f8793071cb383256dfaaa2e650d2276f409eb3d91ba9dfe","observation_id":"e1c9d7cd-bc76-4b46-8667-a08eb233b88c","resolution":{"observed_at":"2026-08-06T19:28:38.107865Z","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":"2026-08-06T19:28:38.255179Z","title":"R., Chornock, R., Soderberg, A","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.255179Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:3cd7d1c9db0824d872f13a491616b282101bc6f41bc92204644d0569c91b1cc6","observation_id":"a302584c-7d18-4a07-9132-d0b84bc559e6","resolution":{"observed_at":"2026-08-06T19:28:38.255179Z","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":"2026-08-06T19:28:38.436794Z","title":"A., Mahabal, A., Masci, F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.436794Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:18ab9f1ba7988dd13e0a5d6432b7e5783acba9bd05c1742f68d07f53f8cff0ca","observation_id":"f00989d5-623d-40ed-be8f-30e558977393","resolution":{"observed_at":"2026-08-06T19:28:38.436794Z","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":"2026-08-06T19:28:38.541210Z","title":"P., et al","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.541210Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:51853c7f0f1a392298632320c706f9036e43267e6255ccf78f1d08c003a435b2","observation_id":"08f28d60-c172-4d28-b32f-38e9624092b7","resolution":{"observed_at":"2026-08-06T19:28:38.541210Z","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":"2026-08-06T19:28:38.702823Z","title":"A., Magnier, E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.702823Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:519b88703fc7cfc57795cdf82bc99538e0763050d9aaff945977bc00c87fbecb","observation_id":"0941778a-e0c5-4f8e-abdd-addf49ae011c","resolution":{"observed_at":"2026-08-06T19:28:38.702823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14990","last_updated":"2023-06-26T18:17:18Z","snapshot_observed_at":"2026-08-13T11:09:00.412787Z","submitted_at":"2023-06-26T18:17:18Z","title":"Massive Black Hole Binaries as LISA Precursors in the Roman High Latitude Time Domain Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.14990","snapshot_observed_at":"2026-08-06T19:28:38.801604Z","title":"2023, arXiv e-prints, arXiv:2306.14990, doi: 10.48550/arXiv.2306.14990","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.801604Z"},"links":{"cited_paper":"/paper/2306.14990","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7e398e3dd06c203b4cabe09583f63a111c2eb0e9da8e5859e9609d141cd98d7d","observation_id":"6646ce66-89e1-4e49-8244-f3cfd76d5200","resolution":{"observed_at":"2026-08-06T19:28:38.801604Z","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":"2026-08-06T19:28:38.922809Z","title":"2023, ApJ, 942, 9, doi: 10.3847/1538-4357/aca283","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:38.922809Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:641865bbdb78f293e83a27cd9cfa2c9796f411e21e2e47cb10f21c5db765b049","observation_id":"29434c94-68e8-44f9-b7d5-6752f09104f1","resolution":{"observed_at":"2026-08-06T19:28:38.922809Z","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":"2026-08-06T19:28:39.072314Z","title":"R., Millman, K","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.072314Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:af2d938ee8ec5229cf1a70f0b79460625c7f83333350d3c6e2da7e1fbda2fc65","observation_id":"271462e4-610d-430b-b405-68eccc1974ec","resolution":{"observed_at":"2026-08-06T19:28:39.072314Z","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":"2026-08-06T19:28:39.213653Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.213653Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:6a5dd5888bd6d1231ff325708ff0401056221baef9a2cc4c3dd4eff9875adb9d","observation_id":"7e10c006-f799-4ef8-a17f-02efb81cdf24","resolution":{"observed_at":"2026-08-06T19:28:39.213653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:47.931430Z","title":"1997, Neural computation, 9, 1735","venue":null,"work_id":"d5567cd2-5968-4b5a-a3c8-06a227923df8","year":1997},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.315818Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:ace298ab7279b0558d994ff8f18b0b7b3c704503d94584e9cb24045d13f66da5","observation_id":"cefd0e36-e1c7-4978-8827-dfc4d6197024","resolution":{"observed_at":"2026-08-06T19:28:47.968876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:39.439168Z","title":"2021, Nature Communications, 12, 2631, doi: 10.1038/s41467-021-22539-9","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.439168Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:10b0d1d2c1d1e0694645d369fc9b29eb6b81c584292e034e4bd3143b6cb50773","observation_id":"a545f1f3-1b7e-46fd-977d-b02863610cc8","resolution":{"observed_at":"2026-08-06T19:28:39.439168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.04163","last_updated":"2018-11-28T18:17:02Z","snapshot_observed_at":"2026-07-06T06:38:28.995451Z","submitted_at":"2018-05-09T14:24:05Z","title":"Hyper-Kamiokande Design Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.04163","snapshot_observed_at":"2026-08-06T19:28:39.597586Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.597586Z"},"links":{"cited_paper":"/paper/1805.04163","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7205ef1cad3012e2624518c351c5c87f4edd6970b10ef57f58cb06e180aea909","observation_id":"abf0bd2e-17ed-4468-9859-2dd598f7bdbe","resolution":{"observed_at":"2026-08-06T19:28:39.597586Z","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":"2026-08-06T19:28:39.732411Z","title":"2001, A&A, 365, L1, doi: 10.1051/0004-6361:20000036","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.732411Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7dd8f3597b23635eaf843cc4e520f11fbad96b5835a6b91e44e1d92e5906510c","observation_id":"bca61640-e079-49ed-b726-40eb5bdfe92c","resolution":{"observed_at":"2026-08-06T19:28:39.732411Z","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":"2026-08-06T19:28:39.824122Z","title":"2016, PASJ, 68, 58, doi: 10.1093/pasj/psw056 —","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.824122Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:4c8608dfdf5c3c11d71badcbdfda19b63fdf06d1c29b9ca758774b101104e6c9","observation_id":"dfc04bec-47ee-474a-a840-229fa373737e","resolution":{"observed_at":"2026-08-06T19:28:39.824122Z","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":"2026-08-06T19:28:39.918166Z","title":"2023, Nature, 618, 500, doi: 10.1038/s41586-023-06096-3 Kovaˇ cevi´ c, M., Pasquato, M., Marelli, M., et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:39.918166Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:d6f8ea9dd12cffacbd6167fb339942d80b52d52c063a931b6ff01684e1701fb0","observation_id":"3b9ed47f-6441-4f93-a44d-339cedbe34e7","resolution":{"observed_at":"2026-08-06T19:28:39.918166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15608","last_updated":"2023-01-18T00:46:55Z","snapshot_observed_at":"2026-08-13T17:24:39.380477Z","submitted_at":"2021-11-30T17:58:57Z","title":"Science with the Ultraviolet Explorer (UVEX)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15608","snapshot_observed_at":"2026-08-06T19:28:40.027056Z","title":"R., Harrison, F","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.027056Z"},"links":{"cited_paper":"/paper/2111.15608","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:d16ce2e769789b11763d8e3af76ee3d7e295b0cbdf52a03d5bdbbaa7042492df","observation_id":"01a94067-2847-45b1-9713-9235aa2bcad1","resolution":{"observed_at":"2026-08-06T19:28:40.027056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.09301","last_updated":"2017-06-19T05:06:39Z","snapshot_observed_at":"2026-08-07T10:52:54.923772Z","submitted_at":"2016-11-28T19:40:26Z","title":"Efficient variational quantum simulator incorporating active error minimisation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.09301","snapshot_observed_at":"2026-08-06T19:28:40.139372Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.139372Z"},"links":{"cited_paper":"/paper/1611.09301","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:8e271269abd05baaa63bb99564bb1dfb1ba921e29b633796f03840000f8428b0","observation_id":"9bde2e80-6561-4be2-b8b7-4b70a05ee8ed","resolution":{"observed_at":"2026-08-06T19:28:40.139372Z","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":"2026-08-06T19:28:40.278750Z","title":"D., et al","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.278750Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:6a81bab0fda8c27ba5a537fff812242866aa614fce23790028836c6cb0afe8c6","observation_id":"87564a92-5da5-4936-b507-1bc729f14f17","resolution":{"observed_at":"2026-08-06T19:28:40.278750Z","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":"10.1088/0004-637x/756/1/27","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A., & Barret, D","venue":"The Astrophysical Journal","work_id":"703680d1-6293-4f62-bc10-4c4b1d693e81","year":2012},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.420624Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:ef93c64f49b9f67f4b2ce8e6eb5879830617225260bf8b6b4d3d178290bb4f61","observation_id":"b36cb7a8-033b-4a83-bf76-b291cd4caf13","resolution":{"observed_at":"2026-08-06T19:28:45.857960Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3847/1538-4357/aaf39b","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"2019, ApJ, 870, 126, doi: 10.3847/1538-4357/aaf39b","venue":"The Astrophysical Journal","work_id":"782ddb79-33a4-4285-9e97-bdeb978e2404","year":2019},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.503819Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:317cf51cfafa32f2aa510ac5d4621be712c75e94a782ef9a33008e4bbd440074","observation_id":"c3c0a594-c918-4992-9184-6f50e0ba5e77","resolution":{"observed_at":"2026-08-06T19:28:45.616343Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:40.649957Z","title":"2020, JCAP, 2020, 050, doi: 10.1088/1475-7516/2020/03/050","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.649957Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:1f31828ec1d93789dcfefd03996eb7c60f7ab52b5c82ebdf25095996bdc2e4ef","observation_id":"8d3ab696-58ca-486f-b11b-4a3b475cde32","resolution":{"observed_at":"2026-08-06T19:28:40.649957Z","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":"2026-08-06T19:28:40.744675Z","title":"2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.744675Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:2aafbe570cec38114bd97fba69117bd9420ff1594806d9b02225e50dae0dc17d","observation_id":"94640638-4a55-47d7-8f9f-741734b1b19c","resolution":{"observed_at":"2026-08-06T19:28:40.744675Z","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":"10.1051/0004-6361/202142952","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"2022, A&A, 664, A81, doi: 10.1051/0004-6361/202142952","venue":"Astronomy and Astrophysics","work_id":"237a11e9-5b15-4245-b26b-6fcecc1bbe3c","year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.885407Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:5a6300227e6977c0f8959914f4c926fb21263697592744338f34e79715f179e2","observation_id":"60e3174c-be1b-4684-8cc0-554c25d15642","resolution":{"observed_at":"2026-08-06T19:28:45.313787Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:40.976766Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:40.976766Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:ce2d3af08b44582e403d0448bdec81e3f61261051f4792a5698d017c9f97c7a4","observation_id":"7e8ceb24-0e89-4bf4-a7b8-4484e19d76de","resolution":{"observed_at":"2026-08-06T19:28:40.976766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:47.715153Z","title":null,"venue":null,"work_id":"ad93a38e-0b32-47b9-adae-8018a2d72d30","year":1992},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.124567Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:9da84907285dabfd6c3ca90411ee2eec4e8a7cf3b2b2fcec694720847a7786d7","observation_id":"2b358f4a-f2c6-40f6-843c-41848a6bed48","resolution":{"observed_at":"2026-08-06T19:28:47.825711Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:41.175859Z","title":"S., Lochner, M., Webb, S., & Narayan, G","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.175859Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:85c13517009fe2a7ee9a52f344b82b75a9704391bd5c3be1c7a0b6e88306a76b","observation_id":"98e4be4b-9a8a-4d40-821b-0b3ab81b45a7","resolution":{"observed_at":"2026-08-06T19:28:41.175859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1306.2307","last_updated":"2013-06-10T20:00:00Z","snapshot_observed_at":"2026-07-06T03:15:32.333110Z","submitted_at":"2013-06-10T20:00:00Z","title":"The Hot and Energetic Universe: A White Paper presenting the science theme motivating the Athena+ mission","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1306.2307","snapshot_observed_at":"2026-08-06T19:28:41.242379Z","title":"2013, arXiv e-prints, arXiv:1306.2307, doi: 10.48550/arXiv.1306.2307","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.242379Z"},"links":{"cited_paper":"/paper/1306.2307","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:b66282e15fd09ab96598964192b77ead697b937ec2e9acb8b0f329e576c03d3b","observation_id":"402d1a57-e7f0-4d17-899f-ae3b4e89c7f8","resolution":{"observed_at":"2026-08-06T19:28:41.242379Z","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":"2026-08-06T19:28:41.401526Z","title":"2014, ApJ, 793, 23, doi: 10.1088/0004-637X/793/1/23","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.401526Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:5c8a6c5a8b4e1c00cb0132b158cfb4c5406082e737c31af4a4a729740eadb89e","observation_id":"794cd55b-02cb-4374-aa28-946fa4e7c205","resolution":{"observed_at":"2026-08-06T19:28:41.401526Z","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":"2026-08-06T19:28:41.481197Z","title":"2024, ApJS, 272, 13, doi: 10.3847/1538-4365/ad344f","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.481197Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:6eacb8d746fbd2bfebfd34b735ec0c776d96d0a29e3e3d233f28863c5d0613b3","observation_id":"f6777ed3-f7e4-4b6e-86fe-44a8f90b3ae2","resolution":{"observed_at":"2026-08-06T19:28:41.481197Z","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":"10.1093/mnras/stac1639","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"L., Lieu, M., & Matzeu, G","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"034467a4-b522-4eb9-809b-18a245886e19","year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.614375Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:2813794ed4940c01523107c72da400bf9964eef25b7850dc9892e11eafc7e89d","observation_id":"aa1b4cb7-3c7a-44a1-95bc-b075e92e5049","resolution":{"observed_at":"2026-08-06T19:28:44.966373Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-06T19:28:41.725795Z","title":"2019, arXiv e-prints, arXiv:1912.01703, doi: 10.48550/arXiv.1912.01703 Peral Garc ´ ıa, D., Cruz-Benito, J., & Jos´ e Garc ´ ıa-Pe˜ nalvo, F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.725795Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:e0a08741e1cb827bd09d9430edf8d06e4cb551c08b9fc67a6fd41bf32e00d1c6","observation_id":"bba07109-c1be-4d15-9b7c-623d4afe557e","resolution":{"observed_at":"2026-08-06T19:28:41.725795Z","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":"2026-08-06T19:28:41.826968Z","title":"2021, ApJ, 913, 60, doi: 10.3847/1538-4357/abf24d S´ anchez-S´ aez, P., Lira, H., Mart ´ ı, L., et al","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.826968Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:e55a875559df6c1d0610bb48fc1fc3eaee31e89e7ab0d30fbc7498f7d944d309","observation_id":"8c9c458f-8588-4bc7-9e2c-3ad555fea36f","resolution":{"observed_at":"2026-08-06T19:28:41.826968Z","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":"2026-08-06T19:28:41.910697Z","title":"2015, Contemporary Physics, 56, 172, doi: 10.1080/00107514.2014.964942","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:41.910697Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:50961cf5ed3247b26af9ed066f39b28fbc202f6eaebbe44feb8123dfd12eb412","observation_id":"3cd321ab-3cdf-4b8e-af31-993c0c659b39","resolution":{"observed_at":"2026-08-06T19:28:41.910697Z","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":"2026-08-06T19:28:42.018718Z","title":"2022, PASJ, 74, 612, doi: 10.1093/pasj/psac023","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.018718Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7742a76ee47619ae9cc9286c028991ffaed4602249ed69619f24646901b16a5a","observation_id":"ca18c13f-7746-4868-ba9c-d612b990dd84","resolution":{"observed_at":"2026-08-06T19:28:42.018718Z","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":"2026-08-06T19:28:42.103950Z","title":"2024, ApJ, 964, 74, doi: 10.3847/1538-4357/ad2704","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.103950Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:d0f4f3a99197dbe6fe3e09c004c9818bc36e50c8b8956431617ae5893e33ff24","observation_id":"c3f7c65e-5363-4b09-9384-31cfe54af470","resolution":{"observed_at":"2026-08-06T19:28:42.103950Z","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":"10.1093/pasj/psab105","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"S., Shimakawa, R., Shimasaku, K., et al","venue":"arXiv (Cornell University)","work_id":"872b9dd4-e578-4f57-8e8c-af4b515fe09f","year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.182897Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:95d4b3a4efa46ec0d39100a955df585495ce88f8334b437d85dd22ea419fea0e","observation_id":"a054d629-94b4-4766-aa50-b07833de0aa7","resolution":{"observed_at":"2026-08-06T19:28:44.603855Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:47.502366Z","title":null,"venue":null,"work_id":"650328bf-997a-4654-84f4-103015227649","year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.296491Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:ea42c5d726e52bf6597dd931a313281c59777769a3ed141bfac93cf19fec5f0c","observation_id":"d3303714-76d8-4a4c-9c10-f19a2d5f96cc","resolution":{"observed_at":"2026-08-06T19:28:47.621121Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:42.421459Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.421459Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:7551739e93b61c95e01e0e66733023dc5252a7a4c345a4c5aa7d4effbfd339fb","observation_id":"668ae089-5a88-4707-9cab-93bf86d6fff0","resolution":{"observed_at":"2026-08-06T19:28:42.421459Z","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":"2026-08-06T19:28:42.480715Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.480715Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:5ed9895c65834ea6c5bee63afdabd00eb5c2c8854465a939537a352788b1bd62","observation_id":"d162f424-c054-42af-81f2-d306009be2ee","resolution":{"observed_at":"2026-08-06T19:28:42.480715Z","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":"2026-08-06T19:28:42.572185Z","title":"1995, A&A, 300, 707","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.572185Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:d03743d4b2abb1ffb6dfc3ff284b909bca3b36336ca136fcc893de70ad4588be","observation_id":"3ad51c15-df54-41f0-86ee-982f75fd181c","resolution":{"observed_at":"2026-08-06T19:28:42.572185Z","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":"2026-08-06T19:28:42.676996Z","title":"2023, MNRAS, 526, 1687, doi: 10.1093/mnras/stad2775","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.676996Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:2ba275fd5d5f4449274ab289359d0257bb02eea13cb255bc19ab05183ec8e64d","observation_id":"2d76a1df-0e81-4720-b6ee-ae456701f4ec","resolution":{"observed_at":"2026-08-06T19:28:42.676996Z","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":"2026-08-06T19:28:42.730658Z","title":"2022, A&A, 657, A138, doi: 10.1051/0004-6361/202141259","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.730658Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:b8bc843d781b148f33f4bf645def5b4bd1eb4259f26075bb1c1e2ffb51d5aa09","observation_id":"fec173f4-07aa-4abf-9e90-cde26d5281e4","resolution":{"observed_at":"2026-08-06T19:28:42.730658Z","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":"2026-08-06T19:28:42.786185Z","title":"D., Lamer, G., et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.786185Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:24c34df9828be11f3773eac8aed7c2a4ed2f576b85304414f2666e7892dd291a","observation_id":"80663e95-e73b-4207-91f2-8b22ce0e727a","resolution":{"observed_at":"2026-08-06T19:28:42.786185Z","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":"2026-08-06T19:28:42.892045Z","title":"F., Barthelmy, S., et al","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.892045Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:f43784753b67f7d6ed542e3574b72d438c875742119821c8d356253bf3a2ce62","observation_id":"47da8045-f410-44d4-9707-0c14376b9b9e","resolution":{"observed_at":"2026-08-06T19:28:42.892045Z","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":"2026-08-06T19:28:42.938717Z","title":"A., Cranmer, M., Berger, E., et al","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:42.938717Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:263f1c8a3b80f1f58fe0edc3f47969a6b2cc8b8be0d217b4f26165f7d3412574","observation_id":"48b06816-1974-4a59-9ea3-14824cbc1515","resolution":{"observed_at":"2026-08-06T19:28:42.938717Z","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":"2026-08-06T19:28:43.019949Z","title":"E., et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.019949Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:886906b7d865f1972d9709f75183da5aace9fab10d703e34503a30bfa4e6e24b","observation_id":"585686b5-4643-4f66-b7e4-48d06ad1f581","resolution":{"observed_at":"2026-08-06T19:28:43.019949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"astro-ph/9909315","last_updated":"1999-09-20T10:44:54Z","snapshot_observed_at":"2026-07-07T01:28:27.106765Z","submitted_at":"1999-09-17T17:36:13Z","title":"The ROSAT All-Sky Survey Bright Source Catalogue","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"astro-ph/9909315","snapshot_observed_at":"2026-08-06T19:28:43.146406Z","title":"1999, A&A, 349, 389, doi: 10.48550/arXiv.astro-ph/9909315 von Kienlin, A., Meegan, C","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.146406Z"},"links":{"cited_paper":"/paper/astro-ph/9909315","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:547c57971541dcfe4145169668355de6d2653ae0bc8d66821335f14c8aa6453b","observation_id":"20876b04-e31b-42c5-9214-a95c4bb17d6d","resolution":{"observed_at":"2026-08-06T19:28:43.146406Z","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":"2026-08-06T19:28:43.279859Z","title":"A., Coriat, M., Traulsen, I., et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.279859Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:26b7469d179f06beed05e3e8484b85866eb09b3beff3c7912dfbfb5952f613d9","observation_id":"55ba33c8-76b3-4623-9db2-0348a1569b55","resolution":{"observed_at":"2026-08-06T19:28:43.279859Z","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":"10.1093/rasti/rzad015","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"RAS Techniques and Instruments","work_id":"5391c0da-8fc9-4404-8759-e18b15cc7801","year":2023},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.407612Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:981c8575d4fecd57a500412f7552aea955fab9dffa74b6aa7c261becbceb5231","observation_id":"a7d3950e-0354-4257-aa88-06406249d62d","resolution":{"observed_at":"2026-08-06T19:28:44.085448Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:28:43.529980Z","title":"A., Luvaul, L","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.529980Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:15bdea776678a00f172105fa9abd7f9dd24f1a34bd27b7b5eb013e0bed8cb182","observation_id":"b826b7a8-7f0e-480f-82cd-333d53d75d6b","resolution":{"observed_at":"2026-08-06T19:28:43.529980Z","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":"2026-08-06T19:28:43.609851Z","title":"L., Eisenhardt, P","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.609851Z"},"links":{"citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:11db0707572178d2ab5ff3853f03ed6cdc90e9e9470b79ef2c944d3786335fc4","observation_id":"b6432b45-ae97-40fd-8f18-78a6865c5c31","resolution":{"observed_at":"2026-08-06T19:28:43.609851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.01783","last_updated":"2020-09-03T16:41:09Z","snapshot_observed_at":"2026-08-10T08:29:51.624290Z","submitted_at":"2020-09-03T16:41:09Z","title":"Quantum Long Short-Term Memory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.01783","snapshot_observed_at":"2026-08-06T19:28:43.721439Z","title":"2015, ApJ, 805, 87, doi: 10.1088/0004-637X/805/2/87 Yen-Chi Chen, S., Yoo, S., & Fang, Y.-L","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T19:28:43.721439Z"},"links":{"cited_paper":"/paper/2009.01783","citing_paper":"/paper/2507.05589"},"observation_digest":"sha256:ad5d122567d5a05aeb4e746148e3a0e17bf47bf2c99f693d6efd97e4574ff701","observation_id":"ac5fb9c0-1e22-4961-a369-32dd440f472a","resolution":{"observed_at":"2026-08-06T19:28:43.721439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.05589","last_updated":"2025-07-08T01:54:16Z","latest_version":1,"primary_category":"astro-ph.HE","snapshot_observed_at":"2026-08-09T22:03:32.490242Z","submitted_at":"2025-07-08T01:54:16Z","title":"Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":56,"verified_exact":6,"verified_fuzzy":1},"total_outbound_references":64},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2507.05589."}