A Machine Learning Inspired Method Reveals the Mass of K2-167 b
Creators
- 1. Massachusetts Institute of Technology, Cambridge, MA
- 2. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA
- 3. Department of Physics and Astronomy, Michigan State University, East Lansing, MI
- 4. Max-Planck-Institut für Astronomie (MPIA), Heidelberg, Germany
- 5. Astrophysics Group, Cavendish Laboratory, Cambridge, UK
- 6. DTU Space, National Space Institute, Technical University of Denmark, Lyngby, Denmark
- 7. Dipartimento di Fisica e Astronomia ``Galileo Galilei'', Università di Padova, Padova, Italy
Description
We report precise radial velocity observations of HD 212657 (= K2-167), a star shown by K2 to host a transiting planet in a 9.97857 day orbit. Using observations from TESS, we refined planet parameters, especially the orbital period. We collected 76 precise radial velocity observations with the HARPS-N spectrograph between August 2015 and October 2016. Although this planet was first found using the transit method in 2015 and validated in 2018, stellar jitter originally limited our ability to measure its mass. In this work, we demonstrate that a new machine learning inspired method can successfully mitigate stellar jitter and reveal the mass of K2-167 b. In the future, these or similar techniques could be widely applied to solar-type (FGK) stars, help measure masses of planets from TESS to fulfill the level 1 science requirement, and eventually help detect habitable-zone Earth-mass exoplanets.
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Additional details
References
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