{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QTZFRDAUB3ZODT5UN7E2UDPSQU","short_pith_number":"pith:QTZFRDAU","schema_version":"1.0","canonical_sha256":"84f2588c140ef2e1cfb46fc9aa0df28501db7fb5b37a665c8cc7b4404b63f656","source":{"kind":"arxiv","id":"1909.06379","version":1},"attestation_state":"computed","paper":{"title":"Neural physical engines for inferring the halo mass distribution function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.CO","authors_text":"Benjamin D. Wandelt, Guilhem Lavaux, Jens Jasche, Michael J. Hudson, Supranta Sarma Boruah, Tom Charnock","submitted_at":"2019-09-13T18:00:02Z","abstract_excerpt":"An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ~Mpc scales, and the distribution of dark matter halos (used as a proxy for galaxies) in the same region. Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo c"},"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":"1909.06379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-09-13T18:00:02Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"e3a95dfe7fe8015cdb37c1e7daa81f0b251b57f06f2915e4624eb7781fa9a5c5","abstract_canon_sha256":"c94ac386a12fa816fc082568b33990f145375dbc7678ec7a305e0f8a8932d9f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:48:44.546874Z","signature_b64":"SFBhrG9/RPDxR/SdfQt/fHoP4UuMrOGGEhaRMMzx8rD8zJax9rZpJ3NsTES3pP5f67G6yxB0cmjGT0uDYIjoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84f2588c140ef2e1cfb46fc9aa0df28501db7fb5b37a665c8cc7b4404b63f656","last_reissued_at":"2026-07-05T00:48:44.546385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:48:44.546385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural physical engines for inferring the halo mass distribution function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.CO","authors_text":"Benjamin D. Wandelt, Guilhem Lavaux, Jens Jasche, Michael J. Hudson, Supranta Sarma Boruah, Tom Charnock","submitted_at":"2019-09-13T18:00:02Z","abstract_excerpt":"An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ~Mpc scales, and the distribution of dark matter halos (used as a proxy for galaxies) in the same region. Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06379","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/1909.06379/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":"1909.06379","created_at":"2026-07-05T00:48:44.546447+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.06379v1","created_at":"2026-07-05T00:48:44.546447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06379","created_at":"2026-07-05T00:48:44.546447+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTZFRDAUB3ZO","created_at":"2026-07-05T00:48:44.546447+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTZFRDAUB3ZODT5U","created_at":"2026-07-05T00:48:44.546447+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTZFRDAU","created_at":"2026-07-05T00:48:44.546447+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.25385","citing_title":"On the Relation Between Field-Level Posteriors, Correlators, and their Likelihoods","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU","json":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU.json","graph_json":"https://pith.science/api/pith-number/QTZFRDAUB3ZODT5UN7E2UDPSQU/graph.json","events_json":"https://pith.science/api/pith-number/QTZFRDAUB3ZODT5UN7E2UDPSQU/events.json","paper":"https://pith.science/paper/QTZFRDAU"},"agent_actions":{"view_html":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU","download_json":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU.json","view_paper":"https://pith.science/paper/QTZFRDAU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.06379&json=true","fetch_graph":"https://pith.science/api/pith-number/QTZFRDAUB3ZODT5UN7E2UDPSQU/graph.json","fetch_events":"https://pith.science/api/pith-number/QTZFRDAUB3ZODT5UN7E2UDPSQU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU/action/storage_attestation","attest_author":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU/action/author_attestation","sign_citation":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU/action/citation_signature","submit_replication":"https://pith.science/pith/QTZFRDAUB3ZODT5UN7E2UDPSQU/action/replication_record"}},"created_at":"2026-07-05T00:48:44.546447+00:00","updated_at":"2026-07-05T00:48:44.546447+00:00"}