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We present a new method for the determination of the surface properties ofairless bodies from measurements of the emitted infrared flux. Our approachuses machine learning techniques to train, validate, and test a neural networkrepresentation of the thermophysical behavior of the atmosphereless body givenshape model, illumination and observational geometry of the remote sensors. Thenetworks are trained on a dataset of thermal simulations of the emittedinfrared flux for different values of surface rock abundance, roughness, andvalues of the thermal inertia of the regolith and of the rock components. Thesesurrogate models are then employed to retrieve the surface thermal propertiesby Markov Chain Monte Carlo Bayesian inversion of observed infrared fluxes. Weapply the method to the inversion of simulated infrared fluxes of asteroid(101195) Bennu -- according to a geometry of observations similar to thoseplanned for NASA's OSIRIS-REx mission -- and infrared observations of asteroid(25143) Itokawa. In both cases, the surface properties of the asteroid -- suchas surface roughness, thermal inertia of the regolith and rock component, andrelative rock abundance -- are retrieved; the contribution from the regolithand rock components are well separated. For the case of Itokawa, we retrieve arock 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 thanthe expected value for LL chondrites, indicating that the rocks on Itokawacould 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 regolithcorresponds to an average regolith particle diameter of about 10 mm,consistently with in situ measurements as well as results from previousstudies.

Constraining the Thermal Properties of Planetary Surfaces using Machine Learning: Application to Airless Bodies