ATM: An Open-Source Tool for Asteroid Thermal Modeling

We publicly release ATM, a Python package designed to model asteroid flux measurements to estimate an asteroid's size, surface temperature distribution, and emissivity. The full multi-dimensional posterior pdf is found using Markov Chain Monte Carlo. Data files with $\sim$ 2.5 million WISE flux measurements for $\sim$ 150,000 asteroids and additional MPC data are also included with the package, as well as Python Jupyter Notebooks with examples of analysis. The entirety of the analysis presented here, including all the figures, tables, and catalogs, can be easily reproduced with these publicly released Notebooks. We show that ATM can match the best-fit size estimates for well-observed asteroids published in 2016 by the NEOWISE team (Mainzer et al. 2016) with a sub-percent bias and a scatter of only 6%. We estimate that the accuracy of WISE-based asteroid size estimates is approximately in the range of 15-20% for most objects. We also study optical data collected by the Sloan Digital Sky Survey (SDSS) and show that correlations of optical colors and WISE-based best-fit model parameters indicate robustness of the latter. Our analysis also gives support to the claim by Harris & Drube (2014) that candidate metallic asteroids can be selected using the best-fit temperature parameter and infrared albedo. We investigate a correlation between SDSS colors and optical albedo derived using WISE-based size estimates and show that this correlation can be used to estimate asteroid sizes with optical data alone, with a precision of about 21% relative to WISE-based size estimates. After accounting for systematic errors, the difference in accuracy between infrared and optical color-based size estimates becomes less than a factor of two. (abridged)
33 pages, 17 figures