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Cataclysmic variables from Sloan Digital Sky Survey – V (2020–2023) identified using machine learning

Inight, Keith; Gänsicke, Boris T; Schwope, Axel; Anderson, Scott F; Breedt, Elmé; Brownstein, Joel R; Demasi, Sebastian; Friedrich, Susanne; Hermes, J J; Long, Knox S; Mulvany, Timothy; Adamane Pallathadka, Gautham; Salvato, Mara; Scaringi, Simone; Schreiber, Matthias R; Stringfellow, Guy S; Thorstensen, John R; Tovmassian, Gagik; Zakamska, Nadia L

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Authors

Keith Inight

Boris T Gänsicke

Axel Schwope

Scott F Anderson

Elmé Breedt

Joel R Brownstein

Sebastian Demasi

Susanne Friedrich

J J Hermes

Knox S Long

Timothy Mulvany

Gautham Adamane Pallathadka

Mara Salvato

Matthias R Schreiber

Guy S Stringfellow

John R Thorstensen

Gagik Tovmassian

Nadia L Zakamska



Citation

Inight, K., Gänsicke, B. T., Schwope, A., Anderson, S. F., Breedt, E., Brownstein, J. R., Demasi, S., Friedrich, S., Hermes, J. J., Long, K. S., Mulvany, T., Adamane Pallathadka, G., Salvato, M., Scaringi, S., Schreiber, M. R., Stringfellow, G. S., Thorstensen, J. R., Tovmassian, G., & Zakamska, N. L. (2025). Cataclysmic variables from Sloan Digital Sky Survey – V (2020–2023) identified using machine learning. Monthly Notices of the Royal Astronomical Society, 536(2), 1057-1076. https://doi.org/10.1093/mnras/stae2524

Journal Article Type Article
Acceptance Date Nov 5, 2024
Online Publication Date Nov 8, 2024
Publication Date 2025-01
Deposit Date Jan 20, 2025
Publicly Available Date Jan 20, 2025
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 536
Issue 2
Pages 1057-1076
DOI https://doi.org/10.1093/mnras/stae2524
Public URL https://durham-repository.worktribe.com/output/3342666

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