Constraining the thermal properties of planetary surfaces using machine learning: Application to airless bodies

Abstract

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 Js −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.

Realistic On-the-fly Outcomes of Planetary Collisions: Machine Learning Applied to Simulations of Giant Impacts

“…Such inversion can be performed by means of Markov Chain Monte Carlo Bayesian inference (Stuart 2010) of observed postcollision scenarios, in which the surrogate models are used to sample the (unknown) posterior distribution of pre-impact conditions. Recent uses of this approach in planetary science include a new technique for constraining the thermal inertias of rock and regolith, and relative rock abundance, on asteroids from observed infrared fluxes (Cambioni et al 2019). Rather than a boutique of scenarios that can solve for the origin of a given planet, there can be an inversion of outcomes.…”

Thermophysical Investigation of Asteroid Surfaces. II. Factors Influencing Grain Size

“…Although the thermal conductivity equations in this work are useful to estimate grain size from thermal inertia, more effort is needed to improve modeling efforts to better resemble asteroid surfaces and regoliths. In particular, considerations for mixed surfaces composed of heterogeneous grains and both solid and porous boulders should be sought (e.g., Cambioni et al 2019).…”

Full-field modeling of heat transfer in asteroid regolith 2: Effects of porosity

“…For Bennu, we assume Cold Bokkeveld-like material properties (Opeil et al, 2020) and use a nominal thermal inertia value of 200 J m −2 K −1 s −1/2 and mean diurnal temperature of 260 K to represent the Hokioi Crater, the location of the Nightingale sample site (Rozitis et al, 2020). For Itokawa, we use a regolith-specific thermal inertia value of 203 J m −2 K −1 s −1/2 and global mean diurnal temperature of 300 K from Cambioni et al (2019). We provide effective particle size estimates in Table 1 for regolith porosities in the range 0.40-0.90 but otherwise do not perform a robust error analysis at this time, given that the aim of this exercise is to compare nominal model predictions.…”