{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LKIQ744TQD6362PXUQ5DTAMOGF","short_pith_number":"pith:LKIQ744T","schema_version":"1.0","canonical_sha256":"5a910ff39380fdbf69f7a43a39818e31656b79cc47a31408d829d9e3f8d36d2f","source":{"kind":"arxiv","id":"2202.06967","version":1},"attestation_state":"computed","paper":{"title":"ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Annalisa Pillepich, Dylan Nelson, Lukas Eisert, Marc Huertas-Company, Ralf S. Klessen, Vicente Rodriguez-Gomez","submitted_at":"2022-02-14T19:00:01Z","abstract_excerpt":"A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history, this merger history remains in principle unobservable. By using the TNG100 simulation of the IllustrisTNG project, we show that it is possible to infer the unobservable stellar assembly and merger history of central galaxies from their observable properties by using machine learning techniques. In particular, in this first paper of ERGO-ML (Extracting Realit"},"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":"2202.06967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.GA","submitted_at":"2022-02-14T19:00:01Z","cross_cats_sorted":[],"title_canon_sha256":"46badec5968e7b2599220ee8cfb4cc61c839f300ed4647f50c0e6fa458de2427","abstract_canon_sha256":"92d0cd5e90d4124cd657e45fa00cb76e462c92420b727b98166333d098fbf08c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:34.795393Z","signature_b64":"uJ4dN/vWVCQqYCxKkR+hShnWIxkhKzL6KH6XvOJT+fG5N6f5YtGGKSpNrosqSW4awvPpvxYdZCpT6kX5MRJbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a910ff39380fdbf69f7a43a39818e31656b79cc47a31408d829d9e3f8d36d2f","last_reissued_at":"2026-07-05T05:20:34.794896Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:34.794896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Annalisa Pillepich, Dylan Nelson, Lukas Eisert, Marc Huertas-Company, Ralf S. Klessen, Vicente Rodriguez-Gomez","submitted_at":"2022-02-14T19:00:01Z","abstract_excerpt":"A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history, this merger history remains in principle unobservable. By using the TNG100 simulation of the IllustrisTNG project, we show that it is possible to infer the unobservable stellar assembly and merger history of central galaxies from their observable properties by using machine learning techniques. In particular, in this first paper of ERGO-ML (Extracting Realit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.06967","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/2202.06967/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":"2202.06967","created_at":"2026-07-05T05:20:34.794955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.06967v1","created_at":"2026-07-05T05:20:34.794955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.06967","created_at":"2026-07-05T05:20:34.794955+00:00"},{"alias_kind":"pith_short_12","alias_value":"LKIQ744TQD63","created_at":"2026-07-05T05:20:34.794955+00:00"},{"alias_kind":"pith_short_16","alias_value":"LKIQ744TQD6362PX","created_at":"2026-07-05T05:20:34.794955+00:00"},{"alias_kind":"pith_short_8","alias_value":"LKIQ744T","created_at":"2026-07-05T05:20:34.794955+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.09209","citing_title":"Performance of morphological classifiers for galaxy mergers compared to current machine learning methods","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF","json":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF.json","graph_json":"https://pith.science/api/pith-number/LKIQ744TQD6362PXUQ5DTAMOGF/graph.json","events_json":"https://pith.science/api/pith-number/LKIQ744TQD6362PXUQ5DTAMOGF/events.json","paper":"https://pith.science/paper/LKIQ744T"},"agent_actions":{"view_html":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF","download_json":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF.json","view_paper":"https://pith.science/paper/LKIQ744T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.06967&json=true","fetch_graph":"https://pith.science/api/pith-number/LKIQ744TQD6362PXUQ5DTAMOGF/graph.json","fetch_events":"https://pith.science/api/pith-number/LKIQ744TQD6362PXUQ5DTAMOGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF/action/storage_attestation","attest_author":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF/action/author_attestation","sign_citation":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF/action/citation_signature","submit_replication":"https://pith.science/pith/LKIQ744TQD6362PXUQ5DTAMOGF/action/replication_record"}},"created_at":"2026-07-05T05:20:34.794955+00:00","updated_at":"2026-07-05T05:20:34.794955+00:00"}