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Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer

Masci, Frank J. and Hoffman, Douglas I. and Grillmair, Carl J. and Cutri, Roc M. (2014) Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer. Astronomical Journal, 148 (1). Art. No. 21. ISSN 0004-6256. doi:10.1088/0004-6256/148/1/21.

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We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately ±2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.

Item Type:Article
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URLURL TypeDescription Paper
Masci, Frank J.0000-0002-8532-9395
Grillmair, Carl J.0000-0003-4072-169X
Cutri, Roc M.0000-0002-0077-2305
Additional Information:© 2014 American Astronomical Society. Received 2014 January 30; accepted 2014 May 7; published 2014 June 13. This work was funded by NASA Astrophysics Data Analysis Program grant NNX13AF37G. We thank the anonymous referee for invaluable comments that helped improve the quality of this manuscript. We are grateful to Max Kuhn for reviewing some of the details of our analyses and descriptions of the algorithms implemented in the R caret package. This publication makes use of data products from The Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Long-term archiving and access to the WISE single-exposure database is funded by NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology, funded by the Planetary Science Division of the National Aeronautics and Space Administration. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Facility: WISE
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
Subject Keywords:methods: statistical; stars: variables: general
Issue or Number:1
Record Number:CaltechAUTHORS:20140808-080029615
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Official Citation:Automated Classification of Periodic Variable Stars Detected by the Wide-field Infrared Survey Explorer Frank J. Masci et al. 2014 The Astronomical Journal 148 21
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:48219
Deposited By: Ruth Sustaita
Deposited On:08 Aug 2014 21:53
Last Modified:10 Nov 2021 18:30

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