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

ABSTRACT 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 the over 2 million SDSS-V spectra obtained with the BOSS spectrograph. The CNN reduced the number of spectra that required visual inspection to $\simeq 2$ per cent of the total. We identified 776 CV spectra among the CNN-selected candidates, plus an additional 27 CV spectra that the CNN misclassified, but that were found serendipitously by human inspection of the data. Analysing the SDSS-V spectroscopy and ancillary data of the 504 CVs in our sample, we report 61 new CVs, spectroscopically confirm 248 and refute 13 published CV candidates, and we report 82 new or improved orbital periods. We discuss the completeness and possible selection biases of the machine learning methodology, as well as the effectiveness of targeting CV candidates within SDSS-V. Finally, we re-assess the space density of CVs, and find $1.2\times 10^{-5}\, \mathrm{pc^{-3}}$.

Authors

Institutions

Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2024-11-08
DOI
https://doi.org/10.1093/mnras/stae2524
Citations
8
Primary Topic
Astrophysics and Cosmic Phenomena
Type
article
Field-Weighted Citation Impact
5.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Gautham Adamane Pallathadka, Knox S. Long, Scott F. Anderson, Guy S. Stringfellow et al.
8 citations
Monthly Notices of the Royal Astronomical Society
Astrophysics and Cosmic Phenomena
5.28
article

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

Gautham Adamane Pallathadka, Knox S. Long, Scott F. Anderson, Guy S. Stringfellow, J. J. Hermes, M. Salvato, Keith Inight, G. Tovmassian, Simone Scaringi, M. R. Schreiber, J. R. Thorstensen, Timothy Mulvany, Nadia L. Zakamska, Sebastian Demasi, Susanne Friedrich, Joel R. Brownstein, E. Breedt, Axel Schwope, Boris T. Gänsicke
article en
8 citations

Abstract

ABSTRACT 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 the over 2 million SDSS-V spectra obtained with the BOSS spectrograph. The CNN reduced the number of spectra that required visual inspection to $\simeq 2$ per cent of the total. We identified 776 CV spectra among the CNN-selected candidates, plus an additional 27 CV spectra that the CNN misclassified, but that were found serendipitously by human inspection of the data. Analysing the SDSS-V spectroscopy and ancillary data of the 504 CVs in our sample, we report 61 new CVs, spectroscopically confirm 248 and refute 13 published CV candidates, and we report 82 new or improved orbital periods. We discuss the completeness and possible selection biases of the machine learning methodology, as well as the effectiveness of targeting CV candidates within SDSS-V. Finally, we re-assess the space density of CVs, and find $1.2\times 10^{-5}\, \mathrm{pc^{-3}}$.

Monthly Notices of the Royal Astronomical SocietyVol. 536(2)
Dartmouth College (US), Boston University (US), Space Telescope Science Institute (US), Johns Hopkins University (US), University of Colorado Boulder (US), Durham University (GB), University of Washington (US), University of Utah (US), University of Cambridge (GB), Leibniz Institute for Astrophysics Potsdam (DE), Astronomical Observatory of Capodimonte (IT), University of Warwick (GB), Max Planck Institute for Extraterrestrial Physics (DE), Laboratory for Atmospheric and Space Physics (US), Université Bourgogne Franche-Comté (FR), Institute of Astronomy (RU), Astronomy and Space (AU), Seattle University (US)
National Science Foundation, Smithsonian Institution, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Yale University, Carnegie Institution of Washington, Ohio State University, California Institute of Technology, Harvard University, University of Utah, University of Washington, Johns Hopkins University, New Mexico State University, Space Telescope Science Institute, European Space Agency, European Commission, Queen's University Belfast, École Polytechnique Fédérale de Lausanne, Queen's University, University of Toronto, Nuclear Safety and Security Commission, Nanjing University, Stockholms Universitet, China National Textile and Apparel Council, Max-Planck-Institut für Astronomie, University of Illinois at Urbana-Champaign, University of Colorado Boulder, Science Mission Directorate, Science and Technology Facilities Council, Deutsches Elektronen-Synchrotron, Leibniz-Gemeinschaft, National Astronomical Observatories, Chinese Academy of Sciences
Openalex Percentile: Top 3%
Astrophysics and Cosmic Phenomena
5.28
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.