{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ISPLJO455ZQXJKWJ4DRBQ73Z2I","short_pith_number":"pith:ISPLJO45","schema_version":"1.0","canonical_sha256":"449eb4bb9dee6174aac9e0e2187f79d23d2940ff454ac526685588f36804b41a","source":{"kind":"arxiv","id":"2203.05583","version":1},"attestation_state":"computed","paper":{"title":"LINNA: Likelihood Inference Neural Network Accelerator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.CO","authors_text":"Andr\\'es N. Salcedo, Chun-Hao To, Eduardo Rozo, Elisabeth Krause, Hao-Yi Wu, Risa H. Wechsler","submitted_at":"2022-03-10T19:00:04Z","abstract_excerpt":"Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analy"},"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":"2203.05583","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.CO","submitted_at":"2022-03-10T19:00:04Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"fcde8b2aa5c4b3fe9d7d82ef5231aa4112a62fe5c7e19ef82cd2105d12f660e6","abstract_canon_sha256":"56345be1fa99741f2bf30b7bc5cd287eccc2b15677eee758da2bc4666d8f0867"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:35:13.377863Z","signature_b64":"TVrCwY6rRJy9IEcpfsI3ssyCkBs9+ZZPx/qnxuvzysneJE29E2pZo8jn7rW5zFcyZ4tsWQFbDKXRYDKayYU1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"449eb4bb9dee6174aac9e0e2187f79d23d2940ff454ac526685588f36804b41a","last_reissued_at":"2026-07-05T05:35:13.377402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:35:13.377402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LINNA: Likelihood Inference Neural Network Accelerator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.CO","authors_text":"Andr\\'es N. Salcedo, Chun-Hao To, Eduardo Rozo, Elisabeth Krause, Hao-Yi Wu, Risa H. Wechsler","submitted_at":"2022-03-10T19:00:04Z","abstract_excerpt":"Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.05583","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.05583/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2203.05583","created_at":"2026-07-05T05:35:13.377470+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.05583v1","created_at":"2026-07-05T05:35:13.377470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.05583","created_at":"2026-07-05T05:35:13.377470+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISPLJO455ZQX","created_at":"2026-07-05T05:35:13.377470+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISPLJO455ZQXJKWJ","created_at":"2026-07-05T05:35:13.377470+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISPLJO45","created_at":"2026-07-05T05:35:13.377470+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/ISPLJO455ZQXJKWJ4DRBQ73Z2I","json":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I.json","graph_json":"https://pith.science/api/pith-number/ISPLJO455ZQXJKWJ4DRBQ73Z2I/graph.json","events_json":"https://pith.science/api/pith-number/ISPLJO455ZQXJKWJ4DRBQ73Z2I/events.json","paper":"https://pith.science/paper/ISPLJO45"},"agent_actions":{"view_html":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I","download_json":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I.json","view_paper":"https://pith.science/paper/ISPLJO45","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.05583&json=true","fetch_graph":"https://pith.science/api/pith-number/ISPLJO455ZQXJKWJ4DRBQ73Z2I/graph.json","fetch_events":"https://pith.science/api/pith-number/ISPLJO455ZQXJKWJ4DRBQ73Z2I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I/action/storage_attestation","attest_author":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I/action/author_attestation","sign_citation":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I/action/citation_signature","submit_replication":"https://pith.science/pith/ISPLJO455ZQXJKWJ4DRBQ73Z2I/action/replication_record"}},"created_at":"2026-07-05T05:35:13.377470+00:00","updated_at":"2026-07-05T05:35:13.377470+00:00"}