Comparison of NEATM-like Models with NASA IRTF and NEOWISE Observations of Near-Earth Asteroid (285263) 1998 QE2

Near-Earth Asteroids (NEAs) are a key testbed for many aspects of solar system science by nature of their proximity to Earth. Investigations into planet formation, asteroid dynamics, and planetary defense initiatives all rely on understanding key characteristics of NEAs such as their sizes, albedo distributions, and regolith properties. Simple thermal models are a commonly used method for analyzing NEA infrared data and determining these key NEA properties. However, these models have inherent limitations due to the simplifying assumptions they make about asteroid shapes and properties. Furthermore, the recent collapse of the Arecibo Telescope limits access to new, high-sensitivity radar data and thus direct NEA size measurements. Additionally, new facilities such as LSST and NEO Surveyor will come online soon, greatly increasing the amount of available NEA photometry. In combination, these issues will further increase the important role simple thermal models play in adding to our knowledge of the NEA population.

Therefore, we present a method for placing tighter constraints on inferred NEA properties using these simple models, so that they may be used more effectively moving forward. By observing an object across multiple viewing geometries and combining relative reflectance spectra with absolute photometry, we are able to place tighter bounds on modeled NEA albedos and thermal inertias than by using single observations alone. We apply this technique to the NEA (285263) 1998 QE2 using a simple thermal model we call our NEATM-like model and data from both the NASA IRTF SpeX instrument and the NEOWISE mission. We determine a narrow albedo and thermal inertia range and compare our results with existing measurements of 1998 QE2 to explore some implications for our NEATM-like model. We further present preliminary results of additional objects. Altogether, our work highlights the limitations of simple thermal models, yet also shows how these models may be better used to understand the NEA population as a whole.