Thermophysical Modeling of Asteroid Surfaces Using Ellipsoid Shape Models

Abstract

Thermophysical Models (TPMs), which have proven to be a powerful tool in the interpretation of the infrared emission of asteroid surfaces, typically make use of a priori obtained shape models and spin axes for use as input boundary conditions. We test then employ a TPM approach -under an assumption of an ellipsoidal shape -that exploits the combination of thermal multi-wavelength observations obtained at preand post-opposition. Thermal infrared data, when available, at these observing circumstances are inherently advantageous in constraining thermal inertia and sense of spin, among other physical traits. We show that, despite the lack of a priori knowledge mentioned above, the size, albedo, and thermal inertia of an object are well-constrained with precision comparable to that of previous techniques. Useful estimates of the surface roughness, shape, and spin direction can also be made, to varying degrees of success. Applying the method to WISE observations, we present best-fit size, albedo, thermal inertia, surface roughness, shape elongation and sense of spin direction for 21 asteroids. We explore the thermal inertia's correlation with asteroid diameter, after accounting for its dependence on the heliocentric distance. /~emaclenn/home (Eric M. MacLennan) 1

Asteroid Thermal Inertia Estimates from Remote Infrared Observations: The Effects of Surface Roughness and Rotation Rate

“…However, recent results from Marciniak et al (2019), on the basis of thermophysical modeling, find that some objects with long periods have very low thermal-inertia values, in contrast to expectations from the work of Harris & Drube (2016). Furthermore, results from thermophysical modeling of remote thermal-infrared observations of some main-belt asteroids (MBAs) are indicative of very low values of thermal inertia compared to those estimated from best-fit η values (Hanuš et al 2018;MacLennan & Emery 2019).…”

Thermophysical Investigation of Asteroid Surfaces I: Characterization of Thermal Inertia

“…If we assume that the DAMIT spin axis estimates have 100% accurate sense of spin, then the TPM has a 76.2% ± 4.3% success rate, based on binomial probability distribution. MacLennan and Emery (2019) demonstrated that the sense of spin success rate is dependent on the thermal inertia, with a success rate of 65 − 80% in the range Γ = 40 − 150 J m −2 K −1 s −1/2 when using spheres. The fact that this agrees with our comparison in this work is encouraging, yet more investigation into model development should be performed in an effort to improve the success rate of constraining the sense of spin using TPMs.…”

“…We use parameterized forms of the energy balance equation and heat diffusion equation (see MacLennan and Emery, 2019, for further details), which reduces the number of TPM variables that are necessary to calculate a unique surface temperature distribution, in order to construct temperature reference tables and reduce the computational time. In this scheme, the necessary information required for rough surface temperature calculation is the bond albedo (A b ), thermal parameter,…”

“…The sense of spin (i.e. retrograde or prograde) for an asteroid can also be estimated using multi-epoch observations (Mueller, 2007;MacLennan and Emery, 2019). Because there exists a time-lag between the period of maximum heating at local noon and when the maximum surface temperature is reached, a morning-afternoon dichotomy is present on surfaces with a non-zero thermal inertia.…”

“…In some cases, we constrain the roughness and object's sense of spin. Comparing our results to the benchmark study of MacLennan and Emery (2019) and other literature works, we assess the ability of this technique to estimate these TPM parameters. We then incorporate diameter, rotation period, and temperature into a unifying multi-factor thermal inertia model that simultaneously accounts for these variables.…”