{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5DNS63J3KQJKKFQLNCJSGOIH3B","short_pith_number":"pith:5DNS63J3","schema_version":"1.0","canonical_sha256":"e8db2f6d3b5412a5160b6893233907d85f0f74a88f5bc56dd4307f4431b49cfc","source":{"kind":"arxiv","id":"2409.10627","version":1},"attestation_state":"computed","paper":{"title":"A machine learning framework to generate star cluster realisations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Alessandro Ballone, George P. Prodan, Giuliano Iorio, Mario Pasquato, Michela Mapelli, Stefano Torniamenti, Ugo Niccol\\`o Di Carlo","submitted_at":"2024-09-16T18:01:02Z","abstract_excerpt":"Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star for"},"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":"2409.10627","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.GA","submitted_at":"2024-09-16T18:01:02Z","cross_cats_sorted":[],"title_canon_sha256":"bb9781218f18d597529ab7670a2b2f90838da021533491798ce9e0d5daf0cdd6","abstract_canon_sha256":"88464fc463523c6afbf17a324e23f3357d55d819836a7c8a1f0c1442f700f377"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:53.979070Z","signature_b64":"9iZNei7rmOfmX1EtV9XaZzPV1FLklEf+V/L5tGM+eg/JV6daJs/TaljehkQRZQol1L1uTdUhLNm/lS/Lqtq2Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8db2f6d3b5412a5160b6893233907d85f0f74a88f5bc56dd4307f4431b49cfc","last_reissued_at":"2026-07-05T09:07:53.978532Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:53.978532Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A machine learning framework to generate star cluster realisations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Alessandro Ballone, George P. Prodan, Giuliano Iorio, Mario Pasquato, Michela Mapelli, Stefano Torniamenti, Ugo Niccol\\`o Di Carlo","submitted_at":"2024-09-16T18:01:02Z","abstract_excerpt":"Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10627","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/2409.10627/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":"2409.10627","created_at":"2026-07-05T09:07:53.978595+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10627v1","created_at":"2026-07-05T09:07:53.978595+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10627","created_at":"2026-07-05T09:07:53.978595+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DNS63J3KQJK","created_at":"2026-07-05T09:07:53.978595+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DNS63J3KQJKKFQL","created_at":"2026-07-05T09:07:53.978595+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DNS63J3","created_at":"2026-07-05T09:07:53.978595+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/5DNS63J3KQJKKFQLNCJSGOIH3B","json":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B.json","graph_json":"https://pith.science/api/pith-number/5DNS63J3KQJKKFQLNCJSGOIH3B/graph.json","events_json":"https://pith.science/api/pith-number/5DNS63J3KQJKKFQLNCJSGOIH3B/events.json","paper":"https://pith.science/paper/5DNS63J3"},"agent_actions":{"view_html":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B","download_json":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B.json","view_paper":"https://pith.science/paper/5DNS63J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10627&json=true","fetch_graph":"https://pith.science/api/pith-number/5DNS63J3KQJKKFQLNCJSGOIH3B/graph.json","fetch_events":"https://pith.science/api/pith-number/5DNS63J3KQJKKFQLNCJSGOIH3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B/action/storage_attestation","attest_author":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B/action/author_attestation","sign_citation":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B/action/citation_signature","submit_replication":"https://pith.science/pith/5DNS63J3KQJKKFQLNCJSGOIH3B/action/replication_record"}},"created_at":"2026-07-05T09:07:53.978595+00:00","updated_at":"2026-07-05T09:07:53.978595+00:00"}