{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:I7THVPPVL2P7RCMEZSHYPKZO2X","short_pith_number":"pith:I7THVPPV","schema_version":"1.0","canonical_sha256":"47e67abdf55e9ff88984cc8f87ab2ed5dcb155f564b06b37d6f5ff0e68da5cfb","source":{"kind":"arxiv","id":"1801.10277","version":1},"attestation_state":"computed","paper":{"title":"Cataloging the Visible Universe through Bayesian Inference at Petascale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"cs.DC","authors_text":"Andreas Noack, David Schlegel, Jarrett Revels, Jeffrey Regier, Jon McAuliffe, Keno Fischer, Kiran Pamnany, Maximilian Lam, Prabhat, Rollin Thomas, Ryan Giordano, Steve Howard","submitted_at":"2018-01-31T02:17:43Z","abstract_excerpt":"Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 minutes. To achie"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1801.10277","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-01-31T02:17:43Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"df9804b2d8223c61d6c7cf88e0a14024834ef5fb4d2b0a90451c27a11a0f45df","abstract_canon_sha256":"95690a79ae00de2332d3b3b96b04da7afdb8c26fc2db6392c63f7232fefa4288"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:41.251922Z","signature_b64":"AKdJ24aQuLUhZ0uqWSGVgr+L1SAsld0oSgjaqWFKRIl5nlXCrQ4h4uVvwPM1EofRpps1AULQF8Q7T0jIm/XMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47e67abdf55e9ff88984cc8f87ab2ed5dcb155f564b06b37d6f5ff0e68da5cfb","last_reissued_at":"2026-05-18T00:24:41.251374Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:41.251374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cataloging the Visible Universe through Bayesian Inference at Petascale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"cs.DC","authors_text":"Andreas Noack, David Schlegel, Jarrett Revels, Jeffrey Regier, Jon McAuliffe, Keno Fischer, Kiran Pamnany, Maximilian Lam, Prabhat, Rollin Thomas, Ryan Giordano, Steve Howard","submitted_at":"2018-01-31T02:17:43Z","abstract_excerpt":"Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 minutes. To achie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10277","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.10277","created_at":"2026-05-18T00:24:41.251470+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.10277v1","created_at":"2026-05-18T00:24:41.251470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10277","created_at":"2026-05-18T00:24:41.251470+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7THVPPVL2P7","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7THVPPVL2P7RCME","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7THVPPV","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X","json":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X.json","graph_json":"https://pith.science/api/pith-number/I7THVPPVL2P7RCMEZSHYPKZO2X/graph.json","events_json":"https://pith.science/api/pith-number/I7THVPPVL2P7RCMEZSHYPKZO2X/events.json","paper":"https://pith.science/paper/I7THVPPV"},"agent_actions":{"view_html":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X","download_json":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X.json","view_paper":"https://pith.science/paper/I7THVPPV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.10277&json=true","fetch_graph":"https://pith.science/api/pith-number/I7THVPPVL2P7RCMEZSHYPKZO2X/graph.json","fetch_events":"https://pith.science/api/pith-number/I7THVPPVL2P7RCMEZSHYPKZO2X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X/action/storage_attestation","attest_author":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X/action/author_attestation","sign_citation":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X/action/citation_signature","submit_replication":"https://pith.science/pith/I7THVPPVL2P7RCMEZSHYPKZO2X/action/replication_record"}},"created_at":"2026-05-18T00:24:41.251470+00:00","updated_at":"2026-05-18T00:24:41.251470+00:00"}