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A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with cecilia.

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Title: A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with cecilia.
Authors: Badenas-Agusti, Mariona1,2,3, (许偲艺), Siyi Xu4, Vanderburg, Andrew2, De, Kishalay2, Dufour, Patrick5, Rogers, Laura K1,4, Hoyos, Susana2,6, Blouin, Simon7, Viaña, Javier2, Bonsor, Amy1, Zuckerman, Ben8
Source: Monthly Notices of the Royal Astronomical Society; Jun2025, Vol. 540 Issue 1, p746-773, 28p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 186167384
AccessLevel: 2
PubType: Academic Journal
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  Data: A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with cecilia.
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  Data: <searchLink fieldCode="JN" term="%22Monthly+Notices+of+the+Royal+Astronomical+Society%22">Monthly Notices of the Royal Astronomical Society</searchLink>; Jun2025, Vol. 540 Issue 1, p746-773, 28p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186167384
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      – Type: doi
        Value: 10.1093/mnras/staf777
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      – Code: eng
        Text: English
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      – TitleFull: A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with cecilia.
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              Text: Jun2025
              Type: published
              Y: 2025
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