{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XTJVMXRYBY733GXXNPBNK7GS46","short_pith_number":"pith:XTJVMXRY","schema_version":"1.0","canonical_sha256":"bcd3565e380e3fbd9af76bc2d57cd2e78bf9a25f4a5579ebbb8e6214557f6bc6","source":{"kind":"arxiv","id":"2505.04681","version":1},"attestation_state":"computed","paper":{"title":"A data-driven approach for star formation parameterization using symbolic regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Blakesley Burkhart, Diane M. Salim, Matthew E. Orr, Miles Cramner, Rachel S. Somerville","submitted_at":"2025-05-07T18:00:00Z","abstract_excerpt":"Star formation (SF) in the interstellar medium (ISM) is fundamental to understanding galaxy evolution and planet formation. However, efforts to develop closed-form analytic expressions that link SF with key influencing physical variables, such as gas density and turbulence, remain challenging. In this work, we leverage recent advancements in machine learning (ML) and use symbolic regression (SR) techniques to produce the first data-driven, ML-discovered analytic expressions for SF using the publicly available FIRE-2 simulation suites. Employing a pipeline based on training the genetic algorith"},"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":"2505.04681","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.GA","submitted_at":"2025-05-07T18:00:00Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"f1730b8e0e0fd315c114a1d85b25063d253f97acd5c2ea09dd21e14aebde41d8","abstract_canon_sha256":"965725025dc7dc7a8adc211f908abf4df7b048e0919d526e4c59b20227252cb4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:11.841400Z","signature_b64":"agxZ5eQsIZpy8os3BapGHsOsnoxkxzzCgN/lRgwjkM+0cXzt+TbQPH1lBKrWTCscC5NaCoQtP7VsXZtLUwiJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bcd3565e380e3fbd9af76bc2d57cd2e78bf9a25f4a5579ebbb8e6214557f6bc6","last_reissued_at":"2026-07-05T11:00:11.840924Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:11.840924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A data-driven approach for star formation parameterization using symbolic regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.GA","authors_text":"Blakesley Burkhart, Diane M. Salim, Matthew E. Orr, Miles Cramner, Rachel S. Somerville","submitted_at":"2025-05-07T18:00:00Z","abstract_excerpt":"Star formation (SF) in the interstellar medium (ISM) is fundamental to understanding galaxy evolution and planet formation. However, efforts to develop closed-form analytic expressions that link SF with key influencing physical variables, such as gas density and turbulence, remain challenging. In this work, we leverage recent advancements in machine learning (ML) and use symbolic regression (SR) techniques to produce the first data-driven, ML-discovered analytic expressions for SF using the publicly available FIRE-2 simulation suites. Employing a pipeline based on training the genetic algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04681","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/2505.04681/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":"2505.04681","created_at":"2026-07-05T11:00:11.840985+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04681v1","created_at":"2026-07-05T11:00:11.840985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04681","created_at":"2026-07-05T11:00:11.840985+00:00"},{"alias_kind":"pith_short_12","alias_value":"XTJVMXRYBY73","created_at":"2026-07-05T11:00:11.840985+00:00"},{"alias_kind":"pith_short_16","alias_value":"XTJVMXRYBY733GXX","created_at":"2026-07-05T11:00:11.840985+00:00"},{"alias_kind":"pith_short_8","alias_value":"XTJVMXRY","created_at":"2026-07-05T11:00:11.840985+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/XTJVMXRYBY733GXXNPBNK7GS46","json":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46.json","graph_json":"https://pith.science/api/pith-number/XTJVMXRYBY733GXXNPBNK7GS46/graph.json","events_json":"https://pith.science/api/pith-number/XTJVMXRYBY733GXXNPBNK7GS46/events.json","paper":"https://pith.science/paper/XTJVMXRY"},"agent_actions":{"view_html":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46","download_json":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46.json","view_paper":"https://pith.science/paper/XTJVMXRY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04681&json=true","fetch_graph":"https://pith.science/api/pith-number/XTJVMXRYBY733GXXNPBNK7GS46/graph.json","fetch_events":"https://pith.science/api/pith-number/XTJVMXRYBY733GXXNPBNK7GS46/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46/action/storage_attestation","attest_author":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46/action/author_attestation","sign_citation":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46/action/citation_signature","submit_replication":"https://pith.science/pith/XTJVMXRYBY733GXXNPBNK7GS46/action/replication_record"}},"created_at":"2026-07-05T11:00:11.840985+00:00","updated_at":"2026-07-05T11:00:11.840985+00:00"}