{"paper":{"title":"Exploring the Limits of Machine Learning Classification of Neutron Star Matter Models","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"Machine learning classifiers separate some neutron star matter models but not others based on mass, radius and oscillation features.","cross_cats":["astro-ph.IM","hep-ph"],"primary_cat":"astro-ph.HE","authors_text":"Wasif Husain","submitted_at":"2025-12-28T13:20:57Z","abstract_excerpt":"We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four representative matter scenarios nucleonic, hyperonic, dark matter admixed, and strange matter models are considered, and a synthetic dataset is constructed from solutions of the Tolman Oppenheimer Volkoff equations under fixed microphysical and transport assumptions.\n  A shallow neural network classifier is trained on physically motivated features, including gravita"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Certain matter scenarios can be separated under controlled assumptions, while others exhibit substantial overlap, reflecting fundamental similarities in their effective equations of state.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The synthetic dataset is generated under fixed microphysical and transport assumptions; if real neutron-star matter deviates from these assumptions or if observational noise is included, the reported separability may not hold.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Machine-learning classification of neutron-star matter models succeeds for some compositions but fails for others due to intrinsic degeneracies in mass-radius-oscillation features under fixed microphysical assumptions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Machine learning classifiers separate some neutron star matter models but not others based on mass, radius and oscillation features.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3fba7adaff5859d6cd0d453d126492aaf480bd032e4055754e449ab634a3ca5b"},"source":{"id":"2512.23758","kind":"arxiv","version":3},"verdict":{"id":"886d0433-321d-4d4f-94c9-74c080c41aea","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T19:34:38.472715Z","strongest_claim":"Certain matter scenarios can be separated under controlled assumptions, while others exhibit substantial overlap, reflecting fundamental similarities in their effective equations of state.","one_line_summary":"Machine-learning classification of neutron-star matter models succeeds for some compositions but fails for others due to intrinsic degeneracies in mass-radius-oscillation features under fixed microphysical assumptions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The synthetic dataset is generated under fixed microphysical and transport assumptions; if real neutron-star matter deviates from these assumptions or if observational noise is included, the reported separability may not hold.","pith_extraction_headline":"Machine learning classifiers separate some neutron star matter models but not others based on mass, radius and oscillation features."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2512.23758/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"}