Long-period High-amplitude Red Variables in the KELT Survey
Abstract
We present a sample of 4132 Mira-like variables (red variables with long periods and high amplitudes) in the Kilodegree Extremely Little Telescope (KELT) survey. Of these, 376 are new Mira-like detections. We used Two Micron All Sky Survey (2MASS) colors to identify candidate asymptotic giant branch stars. We searched for photometric variability among the candidate asymptotic giant branch stars and identified stars that show periodic variability. We selected variables with high amplitudes and strong periodic behavior using a Random Forest classifier. Of the sample of 4132 Mira-like variables, we estimate that 70% are Miras and 30% are semiregular (SR) variables. We also adopt the method of using (W_(RP) - W_(K)) versus (J - K_s) colors in distinguishing between O-rich and C-rich Miras and find it to be an improvement over 2MASS colors.
Additional Information
© 2020. The American Astronomical Society. Received 2019 June 25; revised 2019 December 20; accepted 2020 January 13; published 2020 March 16. We are grateful to the anonymous referee and the AAS statistics consultant for their comments that significantly improved this manuscript. We would like to thank Shazrene Mohamed for her input on the early stages of this work, and R.A.A. thanks Annika Ewigleben and Alyssa Hanes for constructive criticism of the manuscript. R.A.A. was supported by the NSF grants PHY-0849416 and PHY-1359195. R.A.A. would like to acknowledge support from Lehigh University, specifically financial support through the Doctoral Travel Grant for Global Opportunities and the Summer Research Fellowship from the College of Arts and Sciences, and support from the Department of Physics. R.A.A. would also like to acknowledge support from the IAU in the form of a travel grant. M.V.M. was supported by a Dean's Associate Professor Advancement Fellowship from Lehigh University. P.A.W. acknowledges research funding from the South African NRF. This research has made use of the SIMBAD database and the VizieR catalog access tool, both operated at CDS, Strasbourg, France, and the International Variable Star Index (VSX) database, operated at AAVSO, Cambridge, Massachusetts, USA. We have also made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018); NumPy (van der Walt et al. 2011); Scikit-Learn (Pedregosa et al. 2012); and Matplotlib, a Python library for publication quality graphics (Hunter 2007). This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.Attached Files
Published - Arnold_2020_ApJS_247_44.pdf
Accepted Version - 2001.06498.pdf
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Additional details
- Eprint ID
- 101924
- Resolver ID
- CaltechAUTHORS:20200316-150528185
- NSF
- PHY-0849416
- NSF
- PHY-1359195
- Lehigh University
- International Astronomical Union
- National Research Foundation (South Africa)
- Gaia Multilateral Agreement
- Created
-
2020-03-17Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Infrared Processing and Analysis Center (IPAC)