Scaling K2. I. Revised Parameters for 222,088 K2 Stars and a K2 Planet Radius Valley at 1.9 R_⊕
Abstract
Previous measurements of stellar properties for K2 stars in the Ecliptic Plane Input Catalog largely relied on photometry and proper motion measurements, with some added information from available spectra and parallaxes. Combining Gaia DR2 distances with spectroscopic measurements of effective temperatures, surface gravities, and metallicities from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) DR5, we computed updated stellar radii and masses for 26,838 K2 stars. For 195,250 targets without a LAMOST spectrum, we derived stellar parameters using random forest regression on photometric colors trained on the LAMOST sample. In total, we measured spectral types, effective temperatures, surface gravities, metallicities, radii, and masses for 222,088 A, F, G, K, and M-type K2 stars. With these new stellar radii, we performed a simple reanalysis of 299 confirmed and 517 candidate K2 planet radii from Campaigns 1–13, elucidating a distinct planet radius valley around 1.9 R_⊕, a feature thus far only conclusively identified with Kepler planets, and tentatively identified with K2 planets. These updated stellar parameters are a crucial step in the process toward computing K2 planet occurrence rates.
Additional Information
© 2020 The American Astronomical Society. Received 2019 October 7; revised 2020 January 29; accepted 2020 January 30; published 2020 March 10. We thank the anonymous referee for providing several helpful suggestions that improved this manuscript. We would also like to thank Christina Hedges, Geert Barentsen, and Jessie Dotson at the Kepler Guest Observer office for constructive discussions about K2 and this work. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard–Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation grant No. AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation. 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. This work made use of the gaia-kepler.fun cross-match database created by Megan Bedell. This research made use of the cross-match service provided by CDS, Strasbourg. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope; LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. K.H.-U. acknowledges funding from NASA ADAP grant 80NSSC18K0431. Facilities: Exoplanet Archive - , Gaia - , Kepler - , LAMOST - , PS1. - Software: astropy (Astropy Collaboration et al. 2013, 2018), dustmaps (Green et al. 2018), iPython (Pérez & Granger 2007), M_-M_K- (Mann et al. 2019), matplotlib (Hunter 2007), numpy (Oliphant 2015), pandas (McKinney 2010), scikit-learn (Pedregosa et al. 2011), scipy (Virtanen et al. 2020), SpectRes (Carnall 2017).Attached Files
Published - Hardegree-Ullman_2020_ApJS_247_28.pdf
Accepted Version - 2001.11511.pdf
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Additional details
- Eprint ID
- 101845
- Resolver ID
- CaltechAUTHORS:20200310-154033341
- NNX08AR22G
- NASA
- AST-1238877
- NSF
- University of Maryland
- Eotvos Lorand University (ELTE)
- Los Alamos National Laboratory
- Gordon and Betty Moore Foundation
- Gaia Multilateral Agreement
- Created
-
2020-03-10Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Infrared Processing and Analysis Center (IPAC)