{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:L5DA7KX6E5XUP7RCFCJF7UXFKH","short_pith_number":"pith:L5DA7KX6","schema_version":"1.0","canonical_sha256":"5f460faafe276f47fe2228925fd2e551f00bd41dedcaea2c532a8dcf495001d7","source":{"kind":"arxiv","id":"2405.04740","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Alexander Gagliano, Alex I. Malz, Andrew J. Connolly, J. Bryce Kalmbach, John Franklin Crenshaw, Samuel J. Schmidt, The LSST Dark Energy Science Collaboration, Ziang Yan","submitted_at":"2024-05-08T00:59:09Z","abstract_excerpt":"Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of \"true\" redshift posteriors th"},"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":"2405.04740","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2024-05-08T00:59:09Z","cross_cats_sorted":["astro-ph.CO"],"title_canon_sha256":"db74459b787a145e1d7dc2ff67531130102601878776a0d5896bb71280a5a45b","abstract_canon_sha256":"36c468c2a7c61c3b8dfc2ebe91c6440dc5f602cc996583f48a915f589fde7805"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:26:37.657712Z","signature_b64":"s3nh8VxQIrMLa4cCWlL26yBfafuIo1xN2oFNThFBysapW6zfmz+/osCfA/oeg3C2Jj2wf8ItETSZ7zrrk1u6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f460faafe276f47fe2228925fd2e551f00bd41dedcaea2c532a8dcf495001d7","last_reissued_at":"2026-07-05T10:26:37.657198Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:26:37.657198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Alexander Gagliano, Alex I. Malz, Andrew J. Connolly, J. Bryce Kalmbach, John Franklin Crenshaw, Samuel J. Schmidt, The LSST Dark Energy Science Collaboration, Ziang Yan","submitted_at":"2024-05-08T00:59:09Z","abstract_excerpt":"Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of \"true\" redshift posteriors th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04740","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/2405.04740/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":"2405.04740","created_at":"2026-07-05T10:26:37.657259+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04740v1","created_at":"2026-07-05T10:26:37.657259+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04740","created_at":"2026-07-05T10:26:37.657259+00:00"},{"alias_kind":"pith_short_12","alias_value":"L5DA7KX6E5XU","created_at":"2026-07-05T10:26:37.657259+00:00"},{"alias_kind":"pith_short_16","alias_value":"L5DA7KX6E5XUP7RC","created_at":"2026-07-05T10:26:37.657259+00:00"},{"alias_kind":"pith_short_8","alias_value":"L5DA7KX6","created_at":"2026-07-05T10:26:37.657259+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06790","citing_title":"Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH","json":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH.json","graph_json":"https://pith.science/api/pith-number/L5DA7KX6E5XUP7RCFCJF7UXFKH/graph.json","events_json":"https://pith.science/api/pith-number/L5DA7KX6E5XUP7RCFCJF7UXFKH/events.json","paper":"https://pith.science/paper/L5DA7KX6"},"agent_actions":{"view_html":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH","download_json":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH.json","view_paper":"https://pith.science/paper/L5DA7KX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04740&json=true","fetch_graph":"https://pith.science/api/pith-number/L5DA7KX6E5XUP7RCFCJF7UXFKH/graph.json","fetch_events":"https://pith.science/api/pith-number/L5DA7KX6E5XUP7RCFCJF7UXFKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH/action/storage_attestation","attest_author":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH/action/author_attestation","sign_citation":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH/action/citation_signature","submit_replication":"https://pith.science/pith/L5DA7KX6E5XUP7RCFCJF7UXFKH/action/replication_record"}},"created_at":"2026-07-05T10:26:37.657259+00:00","updated_at":"2026-07-05T10:26:37.657259+00:00"}