Asteroid Thermal Inertia Analyzer

Abstract

We developed the new model and corresponding publicly available software for determining the surface thermal inertia (TI) of asteroids [1,2]. We named it ASTERIA (Asteroid Thermal Inertia Analyzer). The model allows TI estimation based mostly on population-modeled input parameters in its basic variant. However, as in general cases the model may not work well if all physical parameters are population-based, we identified the set of critical parameters, which includes the diameter, albedo, and rotation periods of an object.ASTERIA has been validated using data from Bennu and ten other well-characterized NEAs. The results agree well with the literature values, demonstrating the model's reliability for TI analysis.We have identified a set of 38 near-Earth asteroids (NEAs) for which all the input parameters critical for the ASTERIA model to work reliably are available and presented the new TI for those objects. Among these 38 NEAs, 29 are classified as PHAs. It makes our results highly relevant from the planetary defense point of view. Our sample of new TI estimates also includes 31 sub-kilometer-sized asteroids. At the same time, there are only 17 other literature values in this size range, highlighting the importance of the ASTERIA model for determining the surface TI of small asteroids  (see Figure). A general advantage of the ASTERIA model is that it may be applied to smaller asteroids than TPM, because the Yarkovsky effect is more substantial in smaller objects and, therefore, easier to detect. Additionally, TPM requires thermal infrared observations and good shape models, which are currently challenging for asteroids below some 100 m in size. Based on the astrometric measurements and the detection of Yarkovsky-induced acceleration of orbital motion, it is primarily independent of the most widely used approach for the asteroid TI estimations based on TPM. As such, ASTERIA may also serve as a benchmark test to independently verify the results derived from TPM, one of the long-standing challenges of TPM models [3].