Constraining the Thermal Properties of Planetary Surfaces using Machine Learning: Application to Airless Bodies. (arXiv:1902.08631v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Cambioni_S/0/1/0/all/0/1">Saverio Cambioni</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Delbo_M/0/1/0/all/0/1">Marco Delbo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ryan_A/0/1/0/all/0/1">Andrew J. Ryan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Furfaro_R/0/1/0/all/0/1">Roberto Furfaro</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Asphaug_E/0/1/0/all/0/1">Erik Asphaug</a>
We present a new method for the determination of the surface properties of
airless bodies from measurements of the emitted infrared flux. Our approach
uses machine learning techniques to train, validate, and test a neural network
representation of the thermophysical behavior of the atmosphereless body given
shape model, illumination and observational geometry of the remote sensors. The
networks are trained on a dataset of thermal simulations of the emitted
infrared flux for different values of surface rock abundance, roughness, and
values of the thermal inertia of the regolith and of the rock components. These
surrogate models are then employed to retrieve the surface thermal properties
by Markov Chain Monte Carlo Bayesian inversion of observed infrared fluxes. We
apply the method to the inversion of simulated infrared fluxes of asteroid
(101195) Bennu — according to a geometry of observations similar to those
planned for NASA’s OSIRIS-REx mission — and infrared observations of asteroid
(25143) Itokawa. In both cases, the surface properties of the asteroid — such
as surface roughness, thermal inertia of the regolith and rock component, and
relative rock abundance — are retrieved; the contribution from the regolith
and rock components are well separated. For the case of Itokawa, we retrieve a
rock abundance of about 85% for pebbles larger than the diurnal skin depth,
which is about 2 cm. The thermal inertia of the rock is found to be lower than
the expected value for LL chondrites, indicating that the rocks on Itokawa
could be fractured. The average thermal inertia of the surface is around 750 $J
s^{-1/2} K^{-1} m^{-2}$ and the measurement of thermal inertia of the regolith
corresponds to an average regolith particle diameter of about 10 mm,
consistently with in situ measurements as well as results from previous
studies.
We present a new method for the determination of the surface properties of
airless bodies from measurements of the emitted infrared flux. Our approach
uses machine learning techniques to train, validate, and test a neural network
representation of the thermophysical behavior of the atmosphereless body given
shape model, illumination and observational geometry of the remote sensors. The
networks are trained on a dataset of thermal simulations of the emitted
infrared flux for different values of surface rock abundance, roughness, and
values of the thermal inertia of the regolith and of the rock components. These
surrogate models are then employed to retrieve the surface thermal properties
by Markov Chain Monte Carlo Bayesian inversion of observed infrared fluxes. We
apply the method to the inversion of simulated infrared fluxes of asteroid
(101195) Bennu — according to a geometry of observations similar to those
planned for NASA’s OSIRIS-REx mission — and infrared observations of asteroid
(25143) Itokawa. In both cases, the surface properties of the asteroid — such
as surface roughness, thermal inertia of the regolith and rock component, and
relative rock abundance — are retrieved; the contribution from the regolith
and rock components are well separated. For the case of Itokawa, we retrieve a
rock abundance of about 85% for pebbles larger than the diurnal skin depth,
which is about 2 cm. The thermal inertia of the rock is found to be lower than
the expected value for LL chondrites, indicating that the rocks on Itokawa
could be fractured. The average thermal inertia of the surface is around 750 $J
s^{-1/2} K^{-1} m^{-2}$ and the measurement of thermal inertia of the regolith
corresponds to an average regolith particle diameter of about 10 mm,
consistently with in situ measurements as well as results from previous
studies.
http://arxiv.org/icons/sfx.gif
