Forecasted masses for seven thousand KOIs
Jingjing Chen, David Kipping

TL;DR
This paper provides probabilistic mass forecasts for about seven thousand KOIs, aiding observational planning by estimating planetary masses and radial velocity signals, especially for Neptunian and potentially habitable candidates.
Contribution
It introduces a data-driven, probabilistic method to forecast planetary masses for a large number of KOIs, highlighting the limitations of radius-based improvements and identifying promising targets for follow-up.
Findings
Neptunian planets have RV amplitudes around a few m/s.
Mass forecast errors are dominated by model uncertainty, not radius precision.
Several KOIs near the Terran-Neptunian boundary show large RV signals.
Abstract
Recent transit surveys have discovered thousands of planetary candidates with directly measured radii, but only a small fraction have measured masses. Planetary mass is crucial in assessing the feasibility of numerous observational signatures, such as radial velocities (RVs), atmospheres, moons and rings. In the absence of a direct measurement, a data-driven, probabilistic forecast enables observational planning and so here we compute posterior distributions for the forecasted mass of approximately seven thousand Kepler Objects of Interest (KOIs). Our forecasts reveal that the predicted RV amplitudes of Neptunian planets are relatively consistent, as a result of transit survey detection bias, hovering around the few m/s level. We find that mass forecasts are unlikely to improve through more precise planetary radii, with the error budget presently dominated by the intrinsic model…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAstro and Planetary Science · Atmospheric and Environmental Gas Dynamics · Geochemistry and Geologic Mapping
