Cataclysmic variables from Sloan Digital Sky Survey -- V (2020-2023) identified using machine learni...
Cataclysmic variables from Sloan Digital Sky Survey -- V (2020-2023) identified using machine learning
About this item
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Author / Creator
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 , Pallathadka, Gautham A , Salvato, Mara , Scaringi, Simone , Schreiber, Matthias R , Stringfellow, Guy S , Thorstensen, John R , Tovmassian, Gagik and Zakamska, Nadia L
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
SDSS-V is carrying out a dedicated survey for white dwarfs, single and in binaries, and we report the analysis of the spectroscopy of 504 cataclysmic variables (CVs) and CV candidates obtained during the first 34 months of observations of SDSS-V. We developed a convolutional neural network (CNN) to aid with the identification of CV candidates among...
Alternative Titles
Full title
Cataclysmic variables from Sloan Digital Sky Survey -- V (2020-2023) identified using machine learning
Authors, Artists and Contributors
Author / Creator
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
Pallathadka, Gautham A
Salvato, Mara
Scaringi, Simone
Schreiber, Matthias R
Stringfellow, Guy S
Thorstensen, John R
Tovmassian, Gagik
Zakamska, Nadia L
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_3074215489
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3074215489
Other Identifiers
E-ISSN
2331-8422
