{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3XQQG72ONK4LXWCSRMNGNCB54C","short_pith_number":"pith:3XQQG72O","schema_version":"1.0","canonical_sha256":"dde1037f4e6ab8bbd8528b1a66883de08b8683e3d7640a9bc95df2fc086f2d14","source":{"kind":"arxiv","id":"2605.27538","version":1},"attestation_state":"computed","paper":{"title":"VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for QU-Fitting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"Arpan Pal, Preshanth Jagannathan","submitted_at":"2026-05-26T18:11:11Z","abstract_excerpt":"Bayesian QU-fitting is among the most accurate approaches for line-of-sight Faraday inference, but its per-pixel computational cost has made survey-scale application infeasible. QU-fitting is an alternative to Faraday synthesis with comparable accuracy in recovering line-of-sight Faraday components, but it has historically been computationally prohibitive at survey scale. Fitting to the Stokes spectra in $Q$ and $U$ through Bayesian inference is effective but slow. We introduce \\texttt{VROOM-SBI}, which uses simulation-based inference, particularly neural posterior estimation, to speed up infe"},"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":"2605.27538","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-26T18:11:11Z","cross_cats_sorted":["astro-ph.CO","astro-ph.GA"],"title_canon_sha256":"2037894d7f45d547a96ddaca34ecbdfba068fc2becaee4befca8ba0232fefdab","abstract_canon_sha256":"f2d92a75cd4fe9bb7f7e2c132bb8e29662b15a1b1937b227600a9e26d5e4290e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:14.731974Z","signature_b64":"oi/Z8XphfjTFXhqdB28y/ezbiFhgjmpv7wbvJUr7TUV97zSf6yGpTCkXyKNee3/ffo1c9XjsjZZfxXg+/+B0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dde1037f4e6ab8bbd8528b1a66883de08b8683e3d7640a9bc95df2fc086f2d14","last_reissued_at":"2026-05-28T01:04:14.730972Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:14.730972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for QU-Fitting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"Arpan Pal, Preshanth Jagannathan","submitted_at":"2026-05-26T18:11:11Z","abstract_excerpt":"Bayesian QU-fitting is among the most accurate approaches for line-of-sight Faraday inference, but its per-pixel computational cost has made survey-scale application infeasible. QU-fitting is an alternative to Faraday synthesis with comparable accuracy in recovering line-of-sight Faraday components, but it has historically been computationally prohibitive at survey scale. Fitting to the Stokes spectra in $Q$ and $U$ through Bayesian inference is effective but slow. We introduce \\texttt{VROOM-SBI}, which uses simulation-based inference, particularly neural posterior estimation, to speed up infe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27538","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/2605.27538/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":"2605.27538","created_at":"2026-05-28T01:04:14.731098+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27538v1","created_at":"2026-05-28T01:04:14.731098+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27538","created_at":"2026-05-28T01:04:14.731098+00:00"},{"alias_kind":"pith_short_12","alias_value":"3XQQG72ONK4L","created_at":"2026-05-28T01:04:14.731098+00:00"},{"alias_kind":"pith_short_16","alias_value":"3XQQG72ONK4LXWCS","created_at":"2026-05-28T01:04:14.731098+00:00"},{"alias_kind":"pith_short_8","alias_value":"3XQQG72O","created_at":"2026-05-28T01:04:14.731098+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/3XQQG72ONK4LXWCSRMNGNCB54C","json":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C.json","graph_json":"https://pith.science/api/pith-number/3XQQG72ONK4LXWCSRMNGNCB54C/graph.json","events_json":"https://pith.science/api/pith-number/3XQQG72ONK4LXWCSRMNGNCB54C/events.json","paper":"https://pith.science/paper/3XQQG72O"},"agent_actions":{"view_html":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C","download_json":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C.json","view_paper":"https://pith.science/paper/3XQQG72O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27538&json=true","fetch_graph":"https://pith.science/api/pith-number/3XQQG72ONK4LXWCSRMNGNCB54C/graph.json","fetch_events":"https://pith.science/api/pith-number/3XQQG72ONK4LXWCSRMNGNCB54C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C/action/storage_attestation","attest_author":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C/action/author_attestation","sign_citation":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C/action/citation_signature","submit_replication":"https://pith.science/pith/3XQQG72ONK4LXWCSRMNGNCB54C/action/replication_record"}},"created_at":"2026-05-28T01:04:14.731098+00:00","updated_at":"2026-05-28T01:04:14.731098+00:00"}