{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BK3ANGRXOKNWD2QK7LNXH77BSS","short_pith_number":"pith:BK3ANGRX","canonical_record":{"source":{"id":"2605.17445","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T13:32:29Z","cross_cats_sorted":[],"title_canon_sha256":"a21aeeabf6436482468356601e6edc99275c92452bb095d693eb69d1a65f532b","abstract_canon_sha256":"0952730caa9d9662519a1f1f7e5b9c5e6f5c892a68afac24a977f241a36c8b91"},"schema_version":"1.0"},"canonical_sha256":"0ab6069a37729b61ea0afadb73ffe19492e22d1dcdaaf10d293cd78ce35e7c7e","source":{"kind":"arxiv","id":"2605.17445","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17445","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17445v1","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17445","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_12","alias_value":"BK3ANGRXOKNW","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_16","alias_value":"BK3ANGRXOKNWD2QK","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_8","alias_value":"BK3ANGRX","created_at":"2026-05-20T00:04:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BK3ANGRXOKNWD2QK7LNXH77BSS","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17445","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T13:32:29Z","cross_cats_sorted":[],"title_canon_sha256":"a21aeeabf6436482468356601e6edc99275c92452bb095d693eb69d1a65f532b","abstract_canon_sha256":"0952730caa9d9662519a1f1f7e5b9c5e6f5c892a68afac24a977f241a36c8b91"},"schema_version":"1.0"},"canonical_sha256":"0ab6069a37729b61ea0afadb73ffe19492e22d1dcdaaf10d293cd78ce35e7c7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:39.301210Z","signature_b64":"Xk5l06Wjz3thk/0Y9VjVEW7+lGw6ZSulOlALlx5tfcNfGwt4FPwOlTFUMNte+IVwMRXlOS+GHCHfJc5WkoDGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ab6069a37729b61ea0afadb73ffe19492e22d1dcdaaf10d293cd78ce35e7c7e","last_reissued_at":"2026-05-20T00:04:39.300388Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:39.300388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17445","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"giErf6LeYbJDPBE0ZBGcEMByoruO4VYRjSHBB1L44B66KI/c7SXy44ZUcm6jWfUa8pjktakeGqtunewRlJ3iBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:11:27.646128Z"},"content_sha256":"43241ddd74787c34ea349dfadc202b53948958acef2ba19c165ee2e1f48d94aa","schema_version":"1.0","event_id":"sha256:43241ddd74787c34ea349dfadc202b53948958acef2ba19c165ee2e1f48d94aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BK3ANGRXOKNWD2QK7LNXH77BSS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction.","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Chao Liu, Hao Tian, Jialu Nie, Jianjun Chen, Jinzhi Lai, Man I Lam, Ming Yang, Xiaohan Chen, Xin Zhang","submitted_at":"2026-05-17T13:32:29Z","abstract_excerpt":"The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and $>10^5$ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"A hierarchical two-stage deep learning model classifies CSST images into six stellar density categories with 98.83% global accuracy and regresses the number of bright stars (<23.5 mag) with a mean absolute error of 0.0824 dex, enabling density-adapted source extraction.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The six discrete density categories and the training images used to learn them are assumed to be representative of the actual CSST multi-color imaging data distribution across the full dynamic range from voids to the Galactic center.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6fa19d57ae813321c7f135662d2af91ccd1b18a88935be9b22c108c182c6d19e"},"source":{"id":"2605.17445","kind":"arxiv","version":1},"verdict":{"id":"1bb62576-a05a-40bd-940a-f44ad7bedbaa","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T22:41:56.766657Z","strongest_claim":"A hierarchical two-stage deep learning model classifies CSST images into six stellar density categories with 98.83% global accuracy and regresses the number of bright stars (<23.5 mag) with a mean absolute error of 0.0824 dex, enabling density-adapted source extraction.","one_line_summary":"A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The six discrete density categories and the training images used to learn them are assumed to be representative of the actual CSST multi-color imaging data distribution across the full dynamic range from voids to the Galactic center.","pith_extraction_headline":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17445/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T23:01:19.595121Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T22:51:53.855733Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.718825Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.669864Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"299bd88f0be170e642a16945b95d64b7b30cc8f45066e7cc7956929fbc22a828"},"references":{"count":52,"sample":[{"doi":"","year":2018,"title":"M., Abdalla, F., Allam, S., et al","work_id":"e1ea42f9-f90d-43ed-92a4-a50a74c3b37e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"2019, Astronomy & Astrophysics, 627, A23","work_id":"b1e5155e-8927-4bcd-82fe-e590f19171bc","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"2019, in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2623–2631 17","work_id":"a9e60e10-a768-4d6b-81c7-0d27bd2ece8d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"2025, Mock Observations for the CSST Mission: End-to-End Performance Modeling of Optical System, https://arxiv.org/abs/2511.06936","work_id":"c97d6696-8588-4888-95b8-0a72764162d6","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"2025, arXiv preprint arXiv:2511.03064","work_id":"fe1b5421-f112-44e0-add5-3b10638fbf7e","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":52,"snapshot_sha256":"d55e91760e0839f32aa34b7a16116a1142adfc4959adc1f38e11d8f8d78c799e","internal_anchors":5},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1bb62576-a05a-40bd-940a-f44ad7bedbaa"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XTriic6Zi/U58mWH1dQkWnB5PnQCk6MTeNpz7GH18eDg6mtBVobDL3d9QEC8Kys9ru7zS9hCVbVON9S6sZ7dAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:11:27.647399Z"},"content_sha256":"d85810b464ee32f559b67172a2369546e06e9edc918afb3c38e8355e421d8555","schema_version":"1.0","event_id":"sha256:d85810b464ee32f559b67172a2369546e06e9edc918afb3c38e8355e421d8555"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/bundle.json","state_url":"https://pith.science/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T07:11:27Z","links":{"resolver":"https://pith.science/pith/BK3ANGRXOKNWD2QK7LNXH77BSS","bundle":"https://pith.science/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/bundle.json","state":"https://pith.science/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BK3ANGRXOKNWD2QK7LNXH77BSS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BK3ANGRXOKNWD2QK7LNXH77BSS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0952730caa9d9662519a1f1f7e5b9c5e6f5c892a68afac24a977f241a36c8b91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T13:32:29Z","title_canon_sha256":"a21aeeabf6436482468356601e6edc99275c92452bb095d693eb69d1a65f532b"},"schema_version":"1.0","source":{"id":"2605.17445","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17445","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17445v1","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17445","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_12","alias_value":"BK3ANGRXOKNW","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_16","alias_value":"BK3ANGRXOKNWD2QK","created_at":"2026-05-20T00:04:39Z"},{"alias_kind":"pith_short_8","alias_value":"BK3ANGRX","created_at":"2026-05-20T00:04:39Z"}],"graph_snapshots":[{"event_id":"sha256:d85810b464ee32f559b67172a2369546e06e9edc918afb3c38e8355e421d8555","target":"graph","created_at":"2026-05-20T00:04:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"A hierarchical two-stage deep learning model classifies CSST images into six stellar density categories with 98.83% global accuracy and regresses the number of bright stars (<23.5 mag) with a mean absolute error of 0.0824 dex, enabling density-adapted source extraction."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The six discrete density categories and the training images used to learn them are assumed to be representative of the actual CSST multi-color imaging data distribution across the full dynamic range from voids to the Galactic center."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction."}],"snapshot_sha256":"6fa19d57ae813321c7f135662d2af91ccd1b18a88935be9b22c108c182c6d19e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T23:01:19.595121Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T22:51:53.855733Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.718825Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.669864Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17445/integrity.json","findings":[],"snapshot_sha256":"299bd88f0be170e642a16945b95d64b7b30cc8f45066e7cc7956929fbc22a828","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and $>10^5$ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose ","authors_text":"Chao Liu, Hao Tian, Jialu Nie, Jianjun Chen, Jinzhi Lai, Man I Lam, Ming Yang, Xiaohan Chen, Xin Zhang","cross_cats":[],"headline":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T13:32:29Z","title":"Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning"},"references":{"count":52,"internal_anchors":5,"resolved_work":52,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"M., Abdalla, F., Allam, S., et al","work_id":"e1ea42f9-f90d-43ed-92a4-a50a74c3b37e","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"2019, Astronomy & Astrophysics, 627, A23","work_id":"b1e5155e-8927-4bcd-82fe-e590f19171bc","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"2019, in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2623–2631 17","work_id":"a9e60e10-a768-4d6b-81c7-0d27bd2ece8d","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"2025, Mock Observations for the CSST Mission: End-to-End Performance Modeling of Optical System, https://arxiv.org/abs/2511.06936","work_id":"c97d6696-8588-4888-95b8-0a72764162d6","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"2025, arXiv preprint arXiv:2511.03064","work_id":"fe1b5421-f112-44e0-add5-3b10638fbf7e","year":2025}],"snapshot_sha256":"d55e91760e0839f32aa34b7a16116a1142adfc4959adc1f38e11d8f8d78c799e"},"source":{"id":"2605.17445","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T22:41:56.766657Z","id":"1bb62576-a05a-40bd-940a-f44ad7bedbaa","model_set":{"reader":"grok-4.3"},"one_line_summary":"A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A two-stage deep learning model classifies CSST images into six stellar density levels and regresses bright star counts to adapt source extraction.","strongest_claim":"A hierarchical two-stage deep learning model classifies CSST images into six stellar density categories with 98.83% global accuracy and regresses the number of bright stars (<23.5 mag) with a mean absolute error of 0.0824 dex, enabling density-adapted source extraction.","weakest_assumption":"The six discrete density categories and the training images used to learn them are assumed to be representative of the actual CSST multi-color imaging data distribution across the full dynamic range from voids to the Galactic center."}},"verdict_id":"1bb62576-a05a-40bd-940a-f44ad7bedbaa"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:43241ddd74787c34ea349dfadc202b53948958acef2ba19c165ee2e1f48d94aa","target":"record","created_at":"2026-05-20T00:04:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0952730caa9d9662519a1f1f7e5b9c5e6f5c892a68afac24a977f241a36c8b91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T13:32:29Z","title_canon_sha256":"a21aeeabf6436482468356601e6edc99275c92452bb095d693eb69d1a65f532b"},"schema_version":"1.0","source":{"id":"2605.17445","kind":"arxiv","version":1}},"canonical_sha256":"0ab6069a37729b61ea0afadb73ffe19492e22d1dcdaaf10d293cd78ce35e7c7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ab6069a37729b61ea0afadb73ffe19492e22d1dcdaaf10d293cd78ce35e7c7e","first_computed_at":"2026-05-20T00:04:39.300388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:39.300388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xk5l06Wjz3thk/0Y9VjVEW7+lGw6ZSulOlALlx5tfcNfGwt4FPwOlTFUMNte+IVwMRXlOS+GHCHfJc5WkoDGCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:39.301210Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17445","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43241ddd74787c34ea349dfadc202b53948958acef2ba19c165ee2e1f48d94aa","sha256:d85810b464ee32f559b67172a2369546e06e9edc918afb3c38e8355e421d8555"],"state_sha256":"ca7ffca4943428907dc22d2e4eecbb3e3921986a34eb39e7c5d75887cd1d4327"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r4UUmFlO37D2sLoy8o19bEx2Q3AaGCfB8JPErP+YN0jXBWdoQEn+w9R1Sk5OCEq7TyjYMq3aYqdbJItnj3h2Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:11:27.651230Z","bundle_sha256":"4ac0ffca365c6504fa5594d405d4d5cb66b7b63ffb2337942986862024d44a44"}}