J/A+A/630/A135 Beyond the exoplanet mass-radius relation (Ulmer-Moll+, 2019)
Beyond the exoplanet mass-radius relation.
Ulmer-Moll S., Santos N.C., Figueira P., Brinchmann J., Faria J.P.
<Astron. Astrophys. 630, A135 (2019)>
=2019A&A...630A.135U 2019A&A...630A.135U (SIMBAD/NED BibCode)
ADC_Keywords: Exoplanets ; Morphology ; Stars, masses ; Stars, diameters
Keywords: planetary systems - planets and satellites: fundamental parameters -
methods: data analysis
Abstract:
The mass and radius are two fundamental properties to characterize
exoplanets but only for a relatively small fraction of exoplanets are
they both available. The mass is often derived from radial velocity
measurements while the radius is almost always measured with the
transit method. For a large number of exoplanets, either the radius or
the mass is unknown, while the host star has been characterized.
Several mass-radius relations dependent on the planet's type have
been published which often allow to predict the radius, as well as a
bayesian code which forecasts the radius of an exoplanet given the
mass or vice versa.
Our goal is to derive the radius of exoplanets using only observables
extracted from spectra used primarily to determine radial velocities
and spectral parameters. Our objective is to obtain a mass-radius
relation that is independent of the planet's type.
We work with a database of confirmed exoplanets with known radii and
masses as well as the planets from our Solar System. Using random
forests, a machine learning algorithm, we compute the radius of
exoplanets and compare the results to the published radii. Our code,
BEM, is available online. On top of this, we also explore how the
radius estimates compare to previously published mass-radius
relations.
The estimated radii reproduces the spread in radius found for high
mass planets better than previous mass-radius relations. The average
error on the radius is 1.8REarth across the whole range of radii
from 1 to 22REarth. We found that a random forest algorithm is able
to derive reliable radii especially for planets between 4 and
20REarth, for which the error is smaller than 25%. The algorithm has
a low bias but still a high variance, which could be reduced by
limiting the growth of the forest or adding more data.
The random forest algorithm is a promising method to derive exoplanet
properties. We show that the exoplanet's mass and equilibrium
temperature are the relevant properties which constrain the radius,
and do it with higher accuracy than the previous methods.
Description:
Parameters of planets used for the training set.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
training.dat 90 379 Parameters of planets in training set
testing.dat 90 127 Parameters of planets in testing set
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See also:
www.exoplanet.eu : The Extrasolar Planets Encyclopaedia Home Page
Byte-by-byte Description of file: training.dat testing.dat
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Bytes Format Units Label Explanations
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1- 17 A17 --- Name Name of the planet
19- 29 F11.5 Mgeo Mass Planetary mass (1)
31- 38 F8.5 Rgeo Radius Planetary radius (1)
40- 48 F9.5 AU amaj Semi major axis (1)
50- 59 F10.5 K Teq Planet equilibrium temperature
61- 68 F8.5 Lsun L* Stellar luminosity
70- 76 F7.5 Msun M* Stellar mass (1)
78- 84 F7.5 Rsun R* Stellar radius (1)
86- 90 I5 K Teff* Stellar effective temperature (1)
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Note (1): Parameters were taken from the exoplanet.eu database on
April 15, 2019.
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Acknowledgements:
Solene Ulmer-Moll, solene.ulmer-moll(at)astro.up.pt
(End) Patricia Vannier [CDS] 02-Sep-2019