Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published February 2023 | Published
Journal Article Open

Kepler-102: Masses and Compositions for a Super-Earth and Sub-Neptune Orbiting an Active Star


Radial velocity (RV) measurements of transiting multiplanet systems allow us to understand the densities and compositions of planets unlike those in the solar system. Kepler-102, which consists of five tightly packed transiting planets, is a particularly interesting system since it includes a super-Earth (Kepler-102d) and a sub-Neptune-sized planet (Kepler-102e) for which masses can be measured using RVs. Previous work found a high density for Kepler-102d, suggesting a composition similar to that of Mercury, while Kepler-102e was found to have a density typical of sub-Neptune size planets; however, Kepler-102 is an active star, which can interfere with RV mass measurements. To better measure the mass of these two planets, we obtained 111 new RVs using Keck/HIRES and Telescopio Nazionale Galileo/HARPS-N and modeled Kepler-102's activity using quasiperiodic Gaussian process regression. For Kepler-102d, we report a mass upper limit M_d < 5.3 M_⊕ (95% confidence), a best-fit mass M_d = 2.5 ± 1.4 M_⊕, and a density ρ_d = 5.6 ± 3.2 g cm⁻³, which is consistent with a rocky composition similar in density to the Earth. For Kepler-102e we report a mass Mₑ = 4.7 ± 1.7 M_⊕ and a density ρₑ = 1.8 ± 0.7 g cm⁻³. These measurements suggest that Kepler-102e has a rocky core with a thick gaseous envelope comprising 2%–4% of the planet mass and 16%–50% of its radius. Our study is yet another demonstration that accounting for stellar activity in stars with clear rotation signals can yield more accurate planet masses, enabling a more realistic interpretation of planet interiors.

Additional Information

© 2023. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant No. 1842402. C.L.B., L.W., and D.H. acknowledge support from National Aeronautics and Space Administration (grant No. 80NSSC19K0597) issued through the Astrophysics Data Analysis Program. D.H. also acknowledges support from the Alfred P. Sloan Foundation. K.R. acknowledges support from the UK STFC via grant No. ST/V000594/1. E.G. acknowledges support from NASA grant No. 80NSSC20K0957 (Exoplanets Research Program). The HARPS-N project was funded by the Prodex Program of the Swiss Space Office (SSO), the Harvard University Origin of Life Initiative (HUOLI), the Scottish Universities Physics Alliance (SUPA), the University of Geneva, the Smithsonian Astrophysical Observatory (SAO), the Italian National Astrophysical Institute (INAF), University of St. Andrews, Queen's University Belfast, and University of Edinburgh. This work has been supported by the National Aeronautics and Space Administration under grant No. NNX17AB59G, issued through the Exoplanets Research Program. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The specific observations analyzed can be accessed via 10.17909/T9059R. Partly based on observations made with the Italian Telescopio Nazionale Galileo (TNG) operated by the Fundación Galileo Galilei (FGG) of the Istituto Nazionale di Astrofisica (INAF) at the Observatorio del Roque de los Muchachos (La Palma, Canary Islands, Spain). Software: Lightcurve (Lightkurve Collaboration et al. 2018), kiauhoku (Claytor et al. 2020), RadVel (Fulton et al. 2018), PyORBIT (Malavolta et al. 2016, 2018), george (Ambikasaran et al. 2015), emcee (Foreman-Mackey et al. 2013), BurnMan 0.9 (Cottaar et al. 2016), NumPy (Harris et al. 2020), Matplotlib (Hunter 2007), pandas (Wes McKinney 2010), Astropy (Astropy Collaboration et al. 2013, 2018, 2022), SciPy (Virtanen et al. 2020).

Attached Files

Published - Brinkman_2023_AJ_165_74.pdf


Files (2.1 MB)
Name Size Download all
2.1 MB Preview Download

Additional details

August 22, 2023
August 22, 2023