J/A+A/676/A106 Interior structures of 75 exoplanets (Baumeister+, 2023)
ExoMDN: Rapid characterization of exoplanet interior structures with
mixture density networks.
Baumeister P., Tosi N.
<Astron. Astrophys. 676, A106 (2023)>
=2023A&A...676A.106B 2023A&A...676A.106B (SIMBAD/NED BibCode)
ADC_Keywords: Stars, double and multiple ; Exoplanets ; Models ;
Stars, diameters ; Stars, masses
Keywords: planets and satellites: interiors -
planets and satellites: composition - methods: numerical -
methods: statistical
Abstract:
Characterizing the interior structure of exoplanets is essential for
understanding their diversity, formation, and evolution. As the
interior of exoplanets is inaccessible to observations, an inverse
problem must be solved, where numerical structure models need to
conform to observable parameters such as mass and radius. This is a
highly degenerate problem whose solution often relies on
computationally-expensive and time-consuming inference methods such as
Markov Chain Monte Carlo.
We present ExoMDN, a machine-learning model for the interior
characterization of exoplanets based on Mixture Density Networks
(MDN). The model is trained on a large dataset of more than 5.6
million synthetic planets below 25 Earth masses consisting of an iron
core, a silicate mantle, a water and high-pressure ice layer, and a
H/He atmosphere. We employ log-ratio transformations to convert the
interior structure data into a form that the MDN can easily handle.
Given mass, radius, and equilibrium temperature, we show that ExoMDN
can deliver a full posterior distribution of mass fractions and
thicknesses of each planetary layer in under a second on a standard
CPU. Observational uncertainties can be easily accounted for through
repeated predictions from within the uncertainties. We use ExoMDN to
characterise the interior of 22 confirmed exoplanets with mass and
radius uncertainties below 10% and 5% respectively, including the well
studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss
the inclusion of the fluid Love number k2 as an additional
(potential) observable showing how it can significantly reduce the
degeneracy of interior structures. Utilizing the fast predictions of
ExoMDN, we show that measuring k2 with an accuracy of 10% can
constrain the thickness of the Earth's core and mantle to approx. 13%
of the true values.
Description:
This file contains interior structure predictions from ExoMDN for 75
exoplanets below 25Me with radius and mass uncertainties below 10
and 20%, respectively. Given are the mass fraction and thickness
ranges of each planetary layer (iron core, silicate mantle, water
layer, H/He atmosphere) which fit a given mass and radius (including
uncertainties). The error bars for the interior correspond to the 5
and 95 percentiles. For planets with nonsymmetric uncertainties, the
larger value was used.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tablea1.dat 329 75 Interior structures of 75 exoplanets
refs.dat 50 68 References
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See also:
https://github.com/philippbaumeister/ExoMDN : Github repository
Byte-by-byte Description of file: tablea1.dat
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Bytes Format Units Label Explanations
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1- 16 A16 --- Name Planet name (name)
18- 22 F5.3 Rgeo Radius Planet radius (radius)
24- 29 F6.3 Mgeo Mass Planet mass (mass)
31- 35 F5.1 K eqt Planet equilibrium temperature (eqt)
37- 42 A6 --- r_Name Reference (mass and radius), in refs.dat
(r_name)
44- 49 A6 --- r_eqt Reference for eqt values, in refs.dat (r_eqt)
51- 55 F5.3 Rgeo e_Radius Radius uncertainty (E_radius)
57- 61 F5.3 Mgeo e_Mass Mass uncertainty (E_mass)
63- 68 F6.2 K e_eqt Uncertainty of equilibrium temperature
(E_eqt)
70- 79 F10.8 --- Core-rf Median core radius fraction (core_rf)
81- 90 F10.8 --- E_Core-rf Uncertainty on core_rf (upper value)
(Ecorerf) (1)
92-101 F10.8 --- e_Core-rf Uncertainty on core_rf (lower value)
(ecorerf) (2)
103-112 F10.8 --- Mantle-rf Median mantle radius fraction (mantle_rf)
114-123 F10.8 --- E_Mantle-rf Uncertainty on mantle_rf (upper value)
(Emantlerf) (1)
125-134 F10.8 --- e_Mantle-rf Uncertainty on mantle_rf (lower value)
(emantlerf) (2)
136-145 F10.8 --- Water-rf Median water radius fraction (water_rf)
147-156 F10.8 --- E_Water-rf Uncertainty on water_rf (upper value)
(Ewaterrf) (1)
158-167 F10.8 --- e_Water-rf Uncertainty on water_rf (lower value)
(ewaterrf) (2)
169-178 F10.8 --- atm-rf Median atmosphere radius fraction (atm_rf)
180-189 F10.8 --- E_atm-rf Uncertainty on atm_rf (upper value)
(Eatmrf) (1)
191-200 F10.8 --- e_atm-rf Uncertainty on atm_rf (lower value)
(eatmrf) (2)
202-211 F10.8 --- Core-mf Median core mass fraction (core_mf)
213-222 F10.8 --- E_Core-mf Uncertainty on core_mf (upper value)
(Ecoremf) (1)
224-233 F10.8 --- e_Core-mf Uncertainty on core_mf (lower value)
(ecoremf) (2)
235-244 F10.8 --- Mantle-mf Median mantle mass fraction (mantle_mf)
246-255 F10.8 --- E_Mantle-mf Uncertainty on mantle_mf (upper value)
(Emantlemf) (1)
257-266 F10.8 --- e_Mantle-mf Uncertainty on mantle_mf (lower value)
(emantlemf) (2)
268-277 F10.8 --- Water-mf Median water mass fraction (water_mf)
279-288 F10.8 --- E_Water-mf Uncertainty on water_mf (upper value)
(Ewatermf) (1)
290-299 F10.8 --- e_Water-mf Uncertainty on water_mf (lower value)
(ewatermf) (2)
301-309 E9.4 --- atm-mf Median atmosphere mass fraction (atm_mf)
311-319 E9.4 --- E_atm-mf Uncertainty on atm_mf (upper value)
(Eatmmf) (1)
321-329 E9.4 --- e_atm-mf Uncertainty on atm_mf (lower value)
(eatmmf) (2)
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Note (1): based on upper 95th percentile of sample points.
Note (2): based on lower 5th percentile of sample points.
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Byte-by-byte Description of file: refs.dat
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Bytes Format Units Label Explanations
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1- 6 A6 --- Ref Reference code
8- 26 A19 --- Bibcode BibCode
28- 50 A23 --- Aut Author names
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Acknowledgements:
Philipp Baumeister, philipp.baumeister(at)dlr.de
(End) Patricia Vannier [CDS] 14-Jun-2023