{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SENFWQXB56BSQ33GMDU36X65SZ","short_pith_number":"pith:SENFWQXB","canonical_record":{"source":{"id":"2605.16762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-16T02:23:24Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"7890edc39d204082521b4b5dc19e7a316f74bfa455b50b76901bdc19bf4a320b","abstract_canon_sha256":"49aa2bc977e49d73f9b18a075fa095a2f5480c2c938cceb7236d68955d593829"},"schema_version":"1.0"},"canonical_sha256":"911a5b42e1ef83286f6660e9bf5fdd9677ae9e65abcde7096ebbde03f860d161","source":{"kind":"arxiv","id":"2605.16762","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16762","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16762v1","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16762","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_12","alias_value":"SENFWQXB56BS","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_16","alias_value":"SENFWQXB56BSQ33G","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_8","alias_value":"SENFWQXB","created_at":"2026-05-20T00:03:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SENFWQXB56BSQ33GMDU36X65SZ","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-16T02:23:24Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"7890edc39d204082521b4b5dc19e7a316f74bfa455b50b76901bdc19bf4a320b","abstract_canon_sha256":"49aa2bc977e49d73f9b18a075fa095a2f5480c2c938cceb7236d68955d593829"},"schema_version":"1.0"},"canonical_sha256":"911a5b42e1ef83286f6660e9bf5fdd9677ae9e65abcde7096ebbde03f860d161","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:20.533484Z","signature_b64":"nNyvUHWmeEHP8SajyW7Vjdspkto6pwE3RYBd6+ctsPmciafOBG1szGFUVCUihbxJckA1P6niV9wOXanVVrU+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"911a5b42e1ef83286f6660e9bf5fdd9677ae9e65abcde7096ebbde03f860d161","last_reissued_at":"2026-05-20T00:03:20.532085Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:20.532085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16762","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":"2026-05-20T00:03:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UFldQwhSO1qj3iVwcHHONheONBnOiRYeCwYbPphdN3pXMmPSH8imvIJkEGn4mFPOwervhxCIut68NWPt0xGEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T17:33:07.000494Z"},"content_sha256":"c7f880f7910f9904d9ad8ec50ba97a71c9649b7b9669c482cddb4659a55c8f40","schema_version":"1.0","event_id":"sha256:c7f880f7910f9904d9ad8ec50ba97a71c9649b7b9669c482cddb4659a55c8f40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SENFWQXB56BSQ33GMDU36X65SZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties.","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Haitao Miao, Nan Li, Run Wen, Xian-Min Meng, Xingchen Zhou, Xin Zhang, Yan Gong","submitted_at":"2026-05-16T02:23:24Z","abstract_excerpt":"Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra are dispersed directly onto the detector, they are convolved with the 2-dimensional (2D) spatial morphology, which complicates wavelength calibration and consequently degrades the fidelity of subsequent 1-dimensional (1D) spectral extraction. To overcome these limitations, we present a deep learning framework that extracts redshifts directly from 2D slitless"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our model can achieve a precision σ_NMAD=0.0104 and mean uncertainty ⟨E/(1+z_true)⟩=0.0155 for sources with SNR_GI≥1. For sources with SNR_GI higher than 3.0, 5.0 and 10.0, σ_NMAD can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The mock dataset built from HSC-SSP PDR3 images and DESI DR1 SEDs faithfully reproduces the noise, point-spread function, and wavelength calibration properties of actual CSST GV and GI band observations (abstract, paragraph describing dataset construction).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e72969088557e0076a422345200d085146fb992247ecb9f1df8fc7240d9724a0"},"source":{"id":"2605.16762","kind":"arxiv","version":1},"verdict":{"id":"133c099b-044a-484c-b8b7-189819f5727b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:46:06.401661Z","strongest_claim":"Our model can achieve a precision σ_NMAD=0.0104 and mean uncertainty ⟨E/(1+z_true)⟩=0.0155 for sources with SNR_GI≥1. For sources with SNR_GI higher than 3.0, 5.0 and 10.0, σ_NMAD can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys.","one_line_summary":"A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The mock dataset built from HSC-SSP PDR3 images and DESI DR1 SEDs faithfully reproduces the noise, point-spread function, and wavelength calibration properties of actual CSST GV and GI band observations (abstract, paragraph describing dataset construction).","pith_extraction_headline":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16762/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.093967Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:01:04.192807Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.318109Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.450425Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"24b1838c3c95964fb0b9f632298b645f818731d5c44785cfa210ef1b06f2fab1"},"references":{"count":42,"sample":[{"doi":"10.1093/pasj/psx066","year":2018,"title":"2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066","work_id":"adc655f8-0c11-4b14-920f-341c43259701","ref_index":1,"cited_arxiv_id":"1704.05858","is_internal_anchor":true},{"doi":"10.1093/pasj/psab122","year":2022,"title":"2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122","work_id":"4fc2d42d-ccb3-4751-9e2e-54820a204424","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.12942/lrr-2013-6","year":2013,"title":"2013, Living Reviews in Relativity, 16, 6, doi: 10.12942/lrr-2013-6","work_id":"d7d6070e-4630-4ef9-9f08-589c5887796c","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.48550/arxiv.0910.5224","year":2010,"title":"Baryon Acoustic Oscillations","work_id":"03f0002c-4d43-4740-a887-9a162273a4cc","ref_index":4,"cited_arxiv_id":"0910.5224","is_internal_anchor":true},{"doi":"10.1086/344761","year":2003,"title":"R., Lin, H., Lupton, R","work_id":"45da2924-32e4-4ca2-83a6-01e3c7316b46","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":42,"snapshot_sha256":"7ee9cb6482cff1d1a31af8cc93cdc24d97ddeb765d61df069fc423f49a71f67e","internal_anchors":12},"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":"133c099b-044a-484c-b8b7-189819f5727b"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VkT+pimwyZ2axd5kOyPm7DEQENYF0aEth68BQ43Szu+ZkP3fW5nZzKasEw2gGRsH/HTjqL5SAav1Bz+dov6aAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T17:33:07.001656Z"},"content_sha256":"133ddca50179821595d56673562ece5dc01616a2656ccd2788c1248b48d7feef","schema_version":"1.0","event_id":"sha256:133ddca50179821595d56673562ece5dc01616a2656ccd2788c1248b48d7feef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SENFWQXB56BSQ33GMDU36X65SZ/bundle.json","state_url":"https://pith.science/pith/SENFWQXB56BSQ33GMDU36X65SZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SENFWQXB56BSQ33GMDU36X65SZ/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":"2026-05-22T17:33:07Z","links":{"resolver":"https://pith.science/pith/SENFWQXB56BSQ33GMDU36X65SZ","bundle":"https://pith.science/pith/SENFWQXB56BSQ33GMDU36X65SZ/bundle.json","state":"https://pith.science/pith/SENFWQXB56BSQ33GMDU36X65SZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SENFWQXB56BSQ33GMDU36X65SZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SENFWQXB56BSQ33GMDU36X65SZ","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":"49aa2bc977e49d73f9b18a075fa095a2f5480c2c938cceb7236d68955d593829","cross_cats_sorted":["astro-ph.CO"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-16T02:23:24Z","title_canon_sha256":"7890edc39d204082521b4b5dc19e7a316f74bfa455b50b76901bdc19bf4a320b"},"schema_version":"1.0","source":{"id":"2605.16762","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16762","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16762v1","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16762","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_12","alias_value":"SENFWQXB56BS","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_16","alias_value":"SENFWQXB56BSQ33G","created_at":"2026-05-20T00:03:20Z"},{"alias_kind":"pith_short_8","alias_value":"SENFWQXB","created_at":"2026-05-20T00:03:20Z"}],"graph_snapshots":[{"event_id":"sha256:133ddca50179821595d56673562ece5dc01616a2656ccd2788c1248b48d7feef","target":"graph","created_at":"2026-05-20T00:03:20Z","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":"Our model can achieve a precision σ_NMAD=0.0104 and mean uncertainty ⟨E/(1+z_true)⟩=0.0155 for sources with SNR_GI≥1. For sources with SNR_GI higher than 3.0, 5.0 and 10.0, σ_NMAD can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The mock dataset built from HSC-SSP PDR3 images and DESI DR1 SEDs faithfully reproduces the noise, point-spread function, and wavelength calibration properties of actual CSST GV and GI band observations (abstract, paragraph describing dataset construction)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties."}],"snapshot_sha256":"e72969088557e0076a422345200d085146fb992247ecb9f1df8fc7240d9724a0"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.093967Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:01:04.192807Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.318109Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.450425Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16762/integrity.json","findings":[],"snapshot_sha256":"24b1838c3c95964fb0b9f632298b645f818731d5c44785cfa210ef1b06f2fab1","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra are dispersed directly onto the detector, they are convolved with the 2-dimensional (2D) spatial morphology, which complicates wavelength calibration and consequently degrades the fidelity of subsequent 1-dimensional (1D) spectral extraction. To overcome these limitations, we present a deep learning framework that extracts redshifts directly from 2D slitless","authors_text":"Haitao Miao, Nan Li, Run Wen, Xian-Min Meng, Xingchen Zhou, Xin Zhang, Yan Gong","cross_cats":["astro-ph.CO"],"headline":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties.","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-16T02:23:24Z","title":"Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey"},"references":{"count":42,"internal_anchors":12,"resolved_work":42,"sample":[{"cited_arxiv_id":"1704.05858","doi":"10.1093/pasj/psx066","is_internal_anchor":true,"ref_index":1,"title":"2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066","work_id":"adc655f8-0c11-4b14-920f-341c43259701","year":2018},{"cited_arxiv_id":"","doi":"10.1093/pasj/psab122","is_internal_anchor":false,"ref_index":2,"title":"2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122","work_id":"4fc2d42d-ccb3-4751-9e2e-54820a204424","year":2022},{"cited_arxiv_id":"","doi":"10.12942/lrr-2013-6","is_internal_anchor":false,"ref_index":3,"title":"2013, Living Reviews in Relativity, 16, 6, doi: 10.12942/lrr-2013-6","work_id":"d7d6070e-4630-4ef9-9f08-589c5887796c","year":2013},{"cited_arxiv_id":"0910.5224","doi":"10.48550/arxiv.0910.5224","is_internal_anchor":true,"ref_index":4,"title":"Baryon Acoustic Oscillations","work_id":"03f0002c-4d43-4740-a887-9a162273a4cc","year":2010},{"cited_arxiv_id":"","doi":"10.1086/344761","is_internal_anchor":false,"ref_index":5,"title":"R., Lin, H., Lupton, R","work_id":"45da2924-32e4-4ca2-83a6-01e3c7316b46","year":2003}],"snapshot_sha256":"7ee9cb6482cff1d1a31af8cc93cdc24d97ddeb765d61df069fc423f49a71f67e"},"source":{"id":"2605.16762","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T19:46:06.401661Z","id":"133c099b-044a-484c-b8b7-189819f5727b","model_set":{"reader":"grok-4.3"},"one_line_summary":"A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A deep learning model extracts galaxy redshifts directly from 2D slitless spectroscopic images while estimating uncertainties.","strongest_claim":"Our model can achieve a precision σ_NMAD=0.0104 and mean uncertainty ⟨E/(1+z_true)⟩=0.0155 for sources with SNR_GI≥1. For sources with SNR_GI higher than 3.0, 5.0 and 10.0, σ_NMAD can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys.","weakest_assumption":"The mock dataset built from HSC-SSP PDR3 images and DESI DR1 SEDs faithfully reproduces the noise, point-spread function, and wavelength calibration properties of actual CSST GV and GI band observations (abstract, paragraph describing dataset construction)."}},"verdict_id":"133c099b-044a-484c-b8b7-189819f5727b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c7f880f7910f9904d9ad8ec50ba97a71c9649b7b9669c482cddb4659a55c8f40","target":"record","created_at":"2026-05-20T00:03:20Z","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":"49aa2bc977e49d73f9b18a075fa095a2f5480c2c938cceb7236d68955d593829","cross_cats_sorted":["astro-ph.CO"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-16T02:23:24Z","title_canon_sha256":"7890edc39d204082521b4b5dc19e7a316f74bfa455b50b76901bdc19bf4a320b"},"schema_version":"1.0","source":{"id":"2605.16762","kind":"arxiv","version":1}},"canonical_sha256":"911a5b42e1ef83286f6660e9bf5fdd9677ae9e65abcde7096ebbde03f860d161","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"911a5b42e1ef83286f6660e9bf5fdd9677ae9e65abcde7096ebbde03f860d161","first_computed_at":"2026-05-20T00:03:20.532085Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:20.532085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nNyvUHWmeEHP8SajyW7Vjdspkto6pwE3RYBd6+ctsPmciafOBG1szGFUVCUihbxJckA1P6niV9wOXanVVrU+BA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:20.533484Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16762","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7f880f7910f9904d9ad8ec50ba97a71c9649b7b9669c482cddb4659a55c8f40","sha256:133ddca50179821595d56673562ece5dc01616a2656ccd2788c1248b48d7feef"],"state_sha256":"2ae07131fb8fd90d72be28289aabf64a0401c9c356a830d4b2b535c1f5b21e5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZoYW6w5Iean/rxSWyUBhOoN4MHnKNnFvVGq3h08VBPRcuwQqFf6OpGfUADQ+8Gtehy8/DiVG/yUnQd3/giRwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T17:33:07.006508Z","bundle_sha256":"14240ca3860de35300940231ed249d45aa0808c49b2c927ddf77fab4850245e5"}}