[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127772-en":3,"doc-seo-127772-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127772,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning","White dwarfs polluted by exoplanetary material enable direct observation of exoplanet interiors, yet existing spectroscopic surveys struggle because such targets are faint and brightness-limited. The study increases the number of white dwarfs exhibiting multiple atmospheric metals by analyzing 96,134 Gaia DR3 BP/RP (XP) spectra. An unsupervised machine-learning approach, Uniform Manifold Approximation and Projection (UMAP), organizes stars into identifiable spectral regions, with polluted systems appearing as distinct groups. This selection method can raise the number of objects showing five or more metal species by about an order of magnitude, supporting work on exoplanet diversity and geology.","The Astrophysical Journal, 970:181 (12pp), 2024 August 1 © 2024 . The Author(s) . Published by the American Astronomical Society.  \n[https:](https://doi.org/10.3847/1538-4357/ad5d6e)[//](https://doi.org/10.3847/1538-4357/ad5d6e)[doi.org](https://doi.org/10.3847/1538-4357/ad5d6e)[/](https://doi.org/10.3847/1538-4357/ad5d6e)[10.3847](https://doi.org/10.3847/1538-4357/ad5d6e)[/](https://doi.org/10.3847/1538-4357/ad5d6e)[1538-4357](https://doi.org/10.3847/1538-4357/ad5d6e)[/](https://doi.org/10.3847/1538-4357/ad5d6e)[ad5d6e](https://doi.org/10.3847/1538-4357/ad5d6e)  \nHunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and  \nUnsupervised Machine Learning  \nMalia L. Kao 1 , Keith Hawkins 1 , Laura K. Rogers2 , Amy Bonsor2, Bart H. Dunlap 1 , Jason L. Sanders3  ,  \n3  \nM. H. Montgomery 1 , and D. E. Winget 1   \n1 Department of Astronomy, University of Texas at Austin, 2515 Speedway, Austin, TX  \n2 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3  \n78712, USA 0HA, UK  \nDepartment of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK Received 2024 May 20; revised 2024 June 11; accepted 2024 June 13; published 2024 July 31  \nAbstract  \nWhite dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine-learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with five or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.  \nUnified Astronomy Thesaurus concepts: Gaia (2360); White dwarf stars (1799); DZ stars (1848)  \nMaterials only available in the online version of record: machine-readable table  \n1. Introduction  \nThe death of a low-mass (􀀁8 Me) main-sequence (MS) star culminates in the ejection of its outer layers in a planetary nebula and the collapse of its core into a white dwarf (WD) . Typical-mass WDs are extremely dense and have very high surface gravities (log g ∼ 8.0, central density ≈106 g cm−3), equivalent to about 100,000 times that of Earth. As such, WDs are chemically stratified, meaning that lighter elements like Hand He rise to the surface and heavier elements (e.g., C, O, Ca, Mg, Fe) sink to the core. The majority of WDs are expected to consist of carbon−oxygen cores and thin upper layers of He and H that make up only ∼ 1% of the total WD mass. For most WD spectra, we expect the presence of H (DA) or He I (DB) absorption lines, or no spectral lines (DC) if the WD is cold enough to no longer excite atoms above their ground state (􀀁11,000 K for DBs and 􀀁 5000 K for DAs) . However, some WDs, especially cooler WDs, have been found with absorption features from heavier elements in their atmospheres. This has been interpreted as evidence for evolved planetary systems and surviving minor planets (Debes & Sigurdsson 2002; Jura 2003; Zuckerman et al. 2007; Koester et al. 2014) .  \nThe first WD observed with metal pollution in its atmosphere was discovered in 1917 (van Maanen 1917) . It was initially classified as an F-type star since its spectrum featured large amounts of Ca and Fe absorption. Six years later it was reclassified as a WD (Luyten 1923), a newly coined stellar type, but the astrophysical implications of this went unrealized until nearly a century later","cbCaifMpt6YLrfBS","https://ap.wps.com/l/cbCaifMpt6YLrfBS","pdf",1314501,1,12,"English","en",105,"# Introduction\n## White dwarfs, atmospheric stratification, and diffusion timescales\n## Metal pollution: origins and observational constraints\n# Data and methodology\n## Gaia DR3 BP/RP (XP) spectra and sample construction\n## UMAP-based mapping of spectral regions\n# Results and implications\n## Identifying polluted white dwarfs as distinct spectral groups\n## Expected yield increase for multi-metal systems\n## Relevance to exoplanet diversity and geology","[{\"question\":\"Why are polluted white dwarfs important for exoplanet research?\",\"answer\":\"Polluted white dwarfs contain exoplanetary material in their atmospheres, enabling direct probes of exoplanet interiors.\"},{\"question\":\"What limits the discovery of large populations of polluted white dwarfs?\",\"answer\":\"Spectroscopic surveys are constrained by brightness limits, and white dwarfs are typically very faint.\"},{\"question\":\"How does the paper find polluted white dwarfs using Gaia data?\",\"answer\":\"It uses 96,134 Gaia DR3 BP/RP (XP) spectra and applies UMAP, an unsupervised machine-learning method, to cluster stars into identifiable spectral regions where polluted systems form distinct groups.\"}]","Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning | 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are polluted white dwarfs important for exoplanet research?","Question",{"text":76,"@type":77},"Polluted white dwarfs contain exoplanetary material in their atmospheres, enabling direct probes of exoplanet interiors.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limits the discovery of large populations of polluted white dwarfs?",{"text":81,"@type":77},"Spectroscopic surveys are constrained by brightness limits, and white dwarfs are typically very faint.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the paper find polluted white dwarfs using Gaia data?",{"text":85,"@type":77},"It uses 96,134 Gaia DR3 BP/RP (XP) spectra and applies UMAP, an unsupervised machine-learning method, to cluster stars into identifiable spectral regions where polluted systems form distinct 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