{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MPMLJM6W6X6AWJGOWCT7QD5WUK","short_pith_number":"pith:MPMLJM6W","schema_version":"1.0","canonical_sha256":"63d8b4b3d6f5fc0b24ceb0a7f80fb6a2ab0e24e0e4334e063d4baed7d8c31913","source":{"kind":"arxiv","id":"2605.17546","version":1},"attestation_state":"computed","paper":{"title":"Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"One-step generative models recover key galaxy morphology statistics from redshift-conditioned images at orders-of-magnitude lower cost than standard diffusion sampling.","cross_cats":["astro-ph.GA","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Sandro Tacchella, Tianyue Yang, Xiao Xue","submitted_at":"2026-05-17T17:00:39Z","abstract_excerpt":"Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical image synthesis using diffusion models and pixel-MeanFlow. We first review the connections between score-based diffusion models, Flow Matching, one-step generative models, and modern diffusion samplers. We then evaluate DDPM, DDIM, DEIS-AB2, DPM++2M, and one-step pixel-MeanFlow on the GalaxiesML-64 dataset using morphology-based metrics, including elliptici"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.17546","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2026-05-17T17:00:39Z","cross_cats_sorted":["astro-ph.GA","cs.LG"],"title_canon_sha256":"f87a888d023401e9f7d8aba59264fb2f2bab83d1a6b864c3d56a98c06c334adc","abstract_canon_sha256":"bb91f8f8713c75f0556a9fd3ffa544c517cf10064d8e1b624f8626eaf1e9a2fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:45.117201Z","signature_b64":"tnZ2Nvht7F1jQJPi0OT6KgW+bMX2WQSjymWQDfXBj1emChEyTysKAnJqHyXNyLYpRl78mpISfCOK3FxrP7m+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63d8b4b3d6f5fc0b24ceb0a7f80fb6a2ab0e24e0e4334e063d4baed7d8c31913","last_reissued_at":"2026-05-20T00:04:45.116494Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:45.116494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"One-step generative models recover key galaxy morphology statistics from redshift-conditioned images at orders-of-magnitude lower cost than standard diffusion sampling.","cross_cats":["astro-ph.GA","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Sandro Tacchella, Tianyue Yang, Xiao Xue","submitted_at":"2026-05-17T17:00:39Z","abstract_excerpt":"Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical image synthesis using diffusion models and pixel-MeanFlow. We first review the connections between score-based diffusion models, Flow Matching, one-step generative models, and modern diffusion samplers. We then evaluate DDPM, DDIM, DEIS-AB2, DPM++2M, and one-step pixel-MeanFlow on the GalaxiesML-64 dataset using morphology-based metrics, including elliptici"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that one-step generative models can recover key galaxy morphology statistics at orders-of-magnitude lower computational cost, opening a path toward efficient conditional simulators for large cosmological surveys and simulation-based scientific inference.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the chosen morphology-based metrics (ellipticity, semi-major axis, Sérsic index, isophotal area) are sufficient proxies for the scientific usefulness of the generated images in downstream cosmological analyses and inference tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"One-step pixel-MeanFlow models recover key galaxy morphology statistics at orders-of-magnitude lower computational cost than standard DDPM sampling while remaining weaker on fine-grained structure.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"One-step generative models recover key galaxy morphology statistics from redshift-conditioned images at orders-of-magnitude lower cost than standard diffusion sampling.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"df2ff0445a19c57cbb13e17d42ad21a48eb72a678f4533919eac46ff6fd43d4f"},"source":{"id":"2605.17546","kind":"arxiv","version":1},"verdict":{"id":"e8da3c4f-b71e-4e06-9b91-5dc4e530ec00","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T22:06:16.778874Z","strongest_claim":"Our results demonstrate that one-step generative models can recover key galaxy morphology statistics at orders-of-magnitude lower computational cost, opening a path toward efficient conditional simulators for large cosmological surveys and simulation-based scientific inference.","one_line_summary":"One-step pixel-MeanFlow models recover key galaxy morphology statistics at orders-of-magnitude lower computational cost than standard DDPM sampling while remaining weaker on fine-grained structure.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the chosen morphology-based metrics (ellipticity, semi-major axis, Sérsic index, isophotal area) are sufficient proxies for the scientific usefulness of the generated images in downstream cosmological analyses and inference tasks.","pith_extraction_headline":"One-step generative models recover key galaxy morphology statistics from redshift-conditioned images at orders-of-magnitude lower cost than standard diffusion sampling."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17546/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:31:19.587669Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T22:12:23.523658Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.609852Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.544814Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d2b60c7b788edf70618232f4a75bff82b6d669e2c99fc483c791b752aa007bd5"},"references":{"count":65,"sample":[{"doi":"10.1088/0067-0049/203/2/21","year":2012,"title":"doi:10.1088/0067-0049/203/2/21 , eid =","work_id":"fadc132a-8102-44bc-b4cb-27d7b24e86fb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1103/physrevd.105","year":2022,"title":"Accurate effective fluid approximation for ultralight axions","work_id":"977bbb6e-26ea-4a34-a487-d6a6c5e043bf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1051/0004-6361/201834918","year":2019,"title":"2019, A&A, 625, A2, doi:10.1051/0004-6361/201834918","work_id":"bf849814-d0b5-4d98-ab27-110ef6070810","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Second data release of the Hyper Suprime-Cam Subaru Strategic Program","work_id":"17306f27-2942-4e0a-809f-0697d3ba98a4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"arXiv e-prints , keywords =","work_id":"f6113be7-4227-4b07-b837-4fba8bc98567","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":65,"snapshot_sha256":"5728b07909cf3d171803596b273ff3dd9840df8919d18cbcc1473f89049cfea4","internal_anchors":15},"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":"2605.17546","created_at":"2026-05-20T00:04:45.116624+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17546v1","created_at":"2026-05-20T00:04:45.116624+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17546","created_at":"2026-05-20T00:04:45.116624+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPMLJM6W6X6A","created_at":"2026-05-20T00:04:45.116624+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPMLJM6W6X6AWJGO","created_at":"2026-05-20T00:04:45.116624+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPMLJM6W","created_at":"2026-05-20T00:04:45.116624+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/MPMLJM6W6X6AWJGOWCT7QD5WUK","json":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK.json","graph_json":"https://pith.science/api/pith-number/MPMLJM6W6X6AWJGOWCT7QD5WUK/graph.json","events_json":"https://pith.science/api/pith-number/MPMLJM6W6X6AWJGOWCT7QD5WUK/events.json","paper":"https://pith.science/paper/MPMLJM6W"},"agent_actions":{"view_html":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK","download_json":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK.json","view_paper":"https://pith.science/paper/MPMLJM6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17546&json=true","fetch_graph":"https://pith.science/api/pith-number/MPMLJM6W6X6AWJGOWCT7QD5WUK/graph.json","fetch_events":"https://pith.science/api/pith-number/MPMLJM6W6X6AWJGOWCT7QD5WUK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK/action/storage_attestation","attest_author":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK/action/author_attestation","sign_citation":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK/action/citation_signature","submit_replication":"https://pith.science/pith/MPMLJM6W6X6AWJGOWCT7QD5WUK/action/replication_record"}},"created_at":"2026-05-20T00:04:45.116624+00:00","updated_at":"2026-05-20T00:04:45.116624+00:00"}