PROBABILISTIC MASS–RADIUS RELATIONSHIP FOR SUB-NEPTUNE-SIZED PLANETS

By A. Wolfgang, Leslie A. Rogers, Eric B. Ford. 2015

168 citations recorded by the source corpus.

The Kepler Mission has discovered thousands of planets with radii <4 , paving the way for the first statistical studies of the dynamics, formation, and evolution of these sub-Neptunes and super-Earths. Planetary masses are an important physical property for these studies, and yet the vast majority of Kepler planet candidates do not have theirs measured. A key concern is therefore how to map the measured radii to mass estimates in this Earth-to-Neptune size range where there are no Solar System analogs. Previous works have derived deterministic, one-to-one relationships between radius and mass. However, if these planets span a range of compositions as expected, then an intrinsic scatter about this relationship must exist in the population. Here we present the first probabilistic mass–radius relationship (M–R relation) evaluated within a Bayesian framework, which both quantifies this intrinsic dispersion and the uncertainties on the M–R relation parameters. We analyze how the results depend on the radius range of the sample, and on how the masses were measured. Assuming that the M–R relation can be described as a power law with a dispersion that is constant and normally distributed, we find that , a scatter in mass of , and a mass constraint to physically plausible densities, is the “best-fit” probabilistic M–R relation for the sample of RV-measured transiting sub-Neptunes (Rpl < 4 ). More broadly, this work provides a framework for further analyses of the M–R relation and its probable dependencies on period and stellar properties.

Subfields: Astronomy and Astrophysics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ab76360903e5484c718f8741dddf6c30dd759b17

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Citations in the corpus

Inferring probabilistic stellar rotation periods using Gaussian processes (2017, methodology): “Throughout the astronomical literature, hierarchical Bayesian inference has been demonstrated to be a robust method of population inference (e.g. Hogg et al. 2010; Foreman-Mackey et al. 2014a; Wolfgang et al. 2015; Rogers 2015).”
Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, methodology): “For the presentation purpose, we consider two satellite galaxies only and abstract away astrophysical laws and units, but the model can be easily turned into a realistic astrophysics model (Martinez, 2015; Wolfgang et al., 2016).”
WASP-47: A HOT JUPITER SYSTEM WITH TWO ADDITIONAL PLANETS DISCOVERED BY K2 (2015, methodology): “We initialized the chains with random arguments of pericenter and masses drawn from the Hellier et al. (2012) mass posterior for WASP-47 b and the distribution of Wolfgang et al. (2015) for WASP-47 e and WASP47d.”
The Evaporation Valley in the Kepler Planets (2017, background): “…& Lithwick 2014; Jontof-Hutter et al. 2016; Hadden & Lithwick 2016) or radial-velocity (RV) follow-up (e.g. Marcy et al. 2014; Weiss & Marcy 2014) quickly told us that many of these planets had compositions unlike the small terrestrial planets in our own solar system (e.g. Wolfgang et al. 2016).”
How to Characterize Habitable Worlds and Signs of Life (2017, background): “F or p er so na l u se o nl y. 5 to a noisy data set to derive such a limit has severe limitations in this case because it does not properly account for parameters like measurement uncertainty in planetary radii and mass, nondetections, and upper limits (see Wolfgang et al. 2016).”
BREEDING SUPER-EARTHS AND BIRTHING SUPER-PUFFS IN TRANSITIONAL DISKS (2015, result): “…(but not super-Earths) may explain why planet masses and by extension bulk densities obtained from transit-timing variations (TTVs) are systematically low compared to those inferred from radial velocity surveys (Jontof-Hutter et al. 2014; Weiss & Marcy 2014; Steffen 2015; Wolfgang et al. 2015).”
Breaking the chains: hot super-Earth systems from migration and disruption of compact resonant chains (2017, methodology): “As above, we adopt the probabilistic study of Wolfgang et al. (2016).”