{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:U43G54ZZD2JV5LCZMTEWB7JM7M","short_pith_number":"pith:U43G54ZZ","schema_version":"1.0","canonical_sha256":"a7366ef3391e935eac5964c960fd2cfb377245f6baca3e4704bb14a8325b2700","source":{"kind":"arxiv","id":"2311.12110","version":3},"attestation_state":"computed","paper":{"title":"Characterizing Structure Formation through Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Daniel Franco-Barranco, Daniel L\\'opez-Cano, Jens St\\\"ucker, Marcos Pellejero Iba\\~nez, Ra\\'ul E. Angulo","submitted_at":"2023-11-20T19:00:04Z","abstract_excerpt":"Dark matter haloes form from small perturbations to the almost homogeneous density field of the early universe. Although it is known how large these initial perturbations must be to form haloes, it is rather poorly understood how to predict which particles will end up belonging to which halo. However, it is this process that determines the Lagrangian shape of protohaloes and is therefore essential to understand their mass, spin and formation history. Here, we present a machine-learning framework to learn how the protohalo regions of different haloes emerge from the initial density field. This "},"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":"2311.12110","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2023-11-20T19:00:04Z","cross_cats_sorted":[],"title_canon_sha256":"c7fe82b74d81d2fbe4910cb8ec0db47080962da9db7c0e140d78fee74cf363b7","abstract_canon_sha256":"259746bf0fafde30b27951e9e73ba5a85b142b37f442769a33bacabafcd12b54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:45.725472Z","signature_b64":"kD4ofAPkKDcC3KfmgM/XWcW90GC+50OcgUNjIUmTcn2Jpz5jW8CCRe/3dIM2fAQWxcj3RicqpijPc6dBuq8bCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7366ef3391e935eac5964c960fd2cfb377245f6baca3e4704bb14a8325b2700","last_reissued_at":"2026-07-05T07:47:45.724957Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:45.724957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterizing Structure Formation through Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Daniel Franco-Barranco, Daniel L\\'opez-Cano, Jens St\\\"ucker, Marcos Pellejero Iba\\~nez, Ra\\'ul E. Angulo","submitted_at":"2023-11-20T19:00:04Z","abstract_excerpt":"Dark matter haloes form from small perturbations to the almost homogeneous density field of the early universe. Although it is known how large these initial perturbations must be to form haloes, it is rather poorly understood how to predict which particles will end up belonging to which halo. However, it is this process that determines the Lagrangian shape of protohaloes and is therefore essential to understand their mass, spin and formation history. Here, we present a machine-learning framework to learn how the protohalo regions of different haloes emerge from the initial density field. This "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12110","kind":"arxiv","version":3},"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/2311.12110/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":"2311.12110","created_at":"2026-07-05T07:47:45.725022+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.12110v3","created_at":"2026-07-05T07:47:45.725022+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12110","created_at":"2026-07-05T07:47:45.725022+00:00"},{"alias_kind":"pith_short_12","alias_value":"U43G54ZZD2JV","created_at":"2026-07-05T07:47:45.725022+00:00"},{"alias_kind":"pith_short_16","alias_value":"U43G54ZZD2JV5LCZ","created_at":"2026-07-05T07:47:45.725022+00:00"},{"alias_kind":"pith_short_8","alias_value":"U43G54ZZ","created_at":"2026-07-05T07:47:45.725022+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.00049","citing_title":"Segmenting proto-halos with vision transformers","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M","json":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M.json","graph_json":"https://pith.science/api/pith-number/U43G54ZZD2JV5LCZMTEWB7JM7M/graph.json","events_json":"https://pith.science/api/pith-number/U43G54ZZD2JV5LCZMTEWB7JM7M/events.json","paper":"https://pith.science/paper/U43G54ZZ"},"agent_actions":{"view_html":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M","download_json":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M.json","view_paper":"https://pith.science/paper/U43G54ZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.12110&json=true","fetch_graph":"https://pith.science/api/pith-number/U43G54ZZD2JV5LCZMTEWB7JM7M/graph.json","fetch_events":"https://pith.science/api/pith-number/U43G54ZZD2JV5LCZMTEWB7JM7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M/action/storage_attestation","attest_author":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M/action/author_attestation","sign_citation":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M/action/citation_signature","submit_replication":"https://pith.science/pith/U43G54ZZD2JV5LCZMTEWB7JM7M/action/replication_record"}},"created_at":"2026-07-05T07:47:45.725022+00:00","updated_at":"2026-07-05T07:47:45.725022+00:00"}