TL;DR
This paper introduces a probabilistic model for predicting missing mass or radius of astronomical objects, spanning from dwarf planets to stars, with applications in exoplanet characterization and classification.
Contribution
The authors present Forecaster, a probabilistic, unbiased model that predicts mass or radius from limited data, accounting for uncertainties and classifying objects into four categories.
Findings
Detected a transition in the mass-radius relation at approximately 2 Earth masses.
Classified dwarf planets as low-mass Terrans and brown dwarfs as high-mass Jovians.
Confirmed that rocky Super-Earths occupy a narrower parameter space than previously thought.
Abstract
Mass and radius are two of the most fundamental properties of an astronomical object. Increasingly, new planet discoveries are being announced with a measurement of one of these terms, but not both. This has led to a growing need to forecast the missing quantity using the other, especially when predicting the detectability of certain follow-up observations. We present am unbiased forecasting model built upon a probabilistic mass-radius relation conditioned on a sample of 316 well-constrained objects. Our publicly available code, Forecaster, accounts for observational errors, hyper-parameter uncertainties and the intrinsic dispersions observed in the calibration sample. By conditioning our model upon a sample spanning dwarf planets to late-type stars, Forecaster can predict the mass (or radius) from the radius (or mass) for objects covering nine orders-of-magnitude in mass.…
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