{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5MGNYO7LDY7X5VPGTPNSWNONKS","short_pith_number":"pith:5MGNYO7L","schema_version":"1.0","canonical_sha256":"eb0cdc3beb1e3f7ed5e69bdb2b35cd54a4032dd05f831b0d5d0b1769690a61f7","source":{"kind":"arxiv","id":"2505.16553","version":1},"attestation_state":"computed","paper":{"title":"Denoising Milky Way stellar survey data with normalizing flow models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Jason L. Sanders, Ziyang Yan","submitted_at":"2025-05-22T11:40:34Z","abstract_excerpt":"The Gaia dataset has revealed many intricate Milky Way substructures in exquisite detail, including moving groups and the phase spiral. Precise characterisation of these features and detailed comparisons to theoretical models require engaging with Gaia's heteroscedastic noise model, particularly in more distant parts of the Galactic disc and halo. We propose a general, novel machine-learning approach using normalizing flows for denoising density estimation, with particular focus on density estimation from stellar survey data such as that from Gaia. Normalizing flows transform a simple base dis"},"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":"2505.16553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.GA","submitted_at":"2025-05-22T11:40:34Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"375d04e0d33ef6f1b485fb829f6205114a051ddd17650b3c53b557fb2bd827da","abstract_canon_sha256":"28721f8924da547832dc96d77f3912e9d23128aa106270a5a0e985a8bc03bee7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:37.190390Z","signature_b64":"mihNGyq8GF6rWndWyAgzAExFscUiuADYsGcqm8F/7zYY/kEj0vibIOPIAEHanondVRGV0W/pertVtDbVKXY9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb0cdc3beb1e3f7ed5e69bdb2b35cd54a4032dd05f831b0d5d0b1769690a61f7","last_reissued_at":"2026-07-05T11:07:37.189892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:37.189892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Denoising Milky Way stellar survey data with normalizing flow models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Jason L. Sanders, Ziyang Yan","submitted_at":"2025-05-22T11:40:34Z","abstract_excerpt":"The Gaia dataset has revealed many intricate Milky Way substructures in exquisite detail, including moving groups and the phase spiral. Precise characterisation of these features and detailed comparisons to theoretical models require engaging with Gaia's heteroscedastic noise model, particularly in more distant parts of the Galactic disc and halo. We propose a general, novel machine-learning approach using normalizing flows for denoising density estimation, with particular focus on density estimation from stellar survey data such as that from Gaia. Normalizing flows transform a simple base dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16553","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/2505.16553/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":"2505.16553","created_at":"2026-07-05T11:07:37.189959+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16553v1","created_at":"2026-07-05T11:07:37.189959+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16553","created_at":"2026-07-05T11:07:37.189959+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MGNYO7LDY7X","created_at":"2026-07-05T11:07:37.189959+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MGNYO7LDY7X5VPG","created_at":"2026-07-05T11:07:37.189959+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MGNYO7L","created_at":"2026-07-05T11:07:37.189959+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03742","citing_title":"Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows","ref_index":108,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS","json":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS.json","graph_json":"https://pith.science/api/pith-number/5MGNYO7LDY7X5VPGTPNSWNONKS/graph.json","events_json":"https://pith.science/api/pith-number/5MGNYO7LDY7X5VPGTPNSWNONKS/events.json","paper":"https://pith.science/paper/5MGNYO7L"},"agent_actions":{"view_html":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS","download_json":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS.json","view_paper":"https://pith.science/paper/5MGNYO7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16553&json=true","fetch_graph":"https://pith.science/api/pith-number/5MGNYO7LDY7X5VPGTPNSWNONKS/graph.json","fetch_events":"https://pith.science/api/pith-number/5MGNYO7LDY7X5VPGTPNSWNONKS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS/action/storage_attestation","attest_author":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS/action/author_attestation","sign_citation":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS/action/citation_signature","submit_replication":"https://pith.science/pith/5MGNYO7LDY7X5VPGTPNSWNONKS/action/replication_record"}},"created_at":"2026-07-05T11:07:37.189959+00:00","updated_at":"2026-07-05T11:07:37.189959+00:00"}