{"paper":{"title":"Hot DQs, magnetic and metal-polluted white dwarfs: spectroscopic insights from a Gaia machine-learning-selected 500 pc sample","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Machine-learning classifications from low-resolution Gaia spectra accurately identify white dwarf types, showing most massive DB candidates are magnetic white dwarfs or warm DQs instead of genuine helium-rich stars.","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.SR","authors_text":"Aina Ferrer i Burjachs, Alberto Rebassa Mansergas, Enrique Miguel Garc\\'ia Zamora, Santiago Torres Gil","submitted_at":"2026-05-15T18:00:03Z","abstract_excerpt":"The latest Gaia data release provides low-resolution spectra for approximately 100 000 white dwarfs. Though useful for pre-classification, they lack the resolution required for accurate spectral type and parameter determination, motivating spectroscopic follow-up campaigns. In this work, we assess the reliability of machine-learning spectral classifications derived from Gaia spectra through comparison with medium-resolution spectroscopy, determine the nature of objects classified as \"massive helium-rich (DB)\" by automated methods, and characterise the properties of warm and hot DQ (carbon-domi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We find machine-learning classifications are highly accurate (> 90% for spectral types in their training sets), despite the low resolution of Gaia spectra. We show 'massive DBs' to be mostly magnetic white dwarfs and warm DQs, with only 5 of 112 observed (4.46%) confirmed as genuine DBs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Visual inspection of the medium-resolution OSIRIS spectra provides an accurate, objective ground truth for spectral types that is free of significant subjectivity or selection bias in the 255-object sample.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Spectroscopic follow-up validates high accuracy of ML classification for Gaia white dwarf spectra and reclassifies most 'massive DB' candidates as magnetic white dwarfs or warm DQs consistent with merger origins.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Machine-learning classifications from low-resolution Gaia spectra accurately identify white dwarf types, showing most massive DB candidates are magnetic white dwarfs or warm DQs instead of genuine helium-rich stars.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2cba51eb652f8420a64f067bb87a659c26df6d0d31b3515da4f329285871bc85"},"source":{"id":"2605.16493","kind":"arxiv","version":1},"verdict":{"id":"1acc8637-ef23-4717-8456-9ceb5a9f28c0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:23:29.290215Z","strongest_claim":"We find machine-learning classifications are highly accurate (> 90% for spectral types in their training sets), despite the low resolution of Gaia spectra. We show 'massive DBs' to be mostly magnetic white dwarfs and warm DQs, with only 5 of 112 observed (4.46%) confirmed as genuine DBs.","one_line_summary":"Spectroscopic follow-up validates high accuracy of ML classification for Gaia white dwarf spectra and reclassifies most 'massive DB' candidates as magnetic white dwarfs or warm DQs consistent with merger origins.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Visual inspection of the medium-resolution OSIRIS spectra provides an accurate, objective ground truth for spectral types that is free of significant subjectivity or selection bias in the 255-object sample.","pith_extraction_headline":"Machine-learning classifications from low-resolution Gaia spectra accurately identify white dwarf types, showing most massive DB candidates are magnetic white dwarfs or warm DQs instead of genuine helium-rich stars."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16493/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T21:31:20.598255Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.491408Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.101347Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:21:57.012928Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"54be57acb20b0c66ddc77846866b2a6739922e0e2b7c8131338402e618ad917f"},"references":{"count":51,"sample":[{"doi":"","year":2010,"title":"Althaus, L. 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F. 2019, ApJ, 878, 63","work_id":"74e2f3a5-894a-450f-8066-bbcb3b1d248f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":51,"snapshot_sha256":"ca77ae9a4740697e9bc585c2730c914f940e2c8a0a12886e1b0f484b160d9164","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"91621f34f9d238cca2f7ec40b40657e329df187642958bfb1746f198091b6b60"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}