We publicly release ATM, a Python package designed to model asteroid flux measurements to estimate an asteroid's size, surface temperature distribution, and emissivity. A number of the most popular static asteroid thermal models (NEATM, STM, FRM) are implemented with the reflected solar light contribution and Kirchhoff's law accounted for. Priors for fitted parameters can be easily specified and the solution, including the full multi-dimensional posterior probability density function, is found using Markov Chain Monte Carlo (MCMC). We describe the package's architecture and discuss model and fitting validation. Data files with ~10 million WISE flux measurements for ~150,000 unique asteroids and additional Minor Planet Center data are also included with the package, as well as Python Jupyter Notebooks with examples of how to select subsamples of objects, filter and process data, and use ATM to fit for the desired model parameters. 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%. Our analysis of various sources of random and systematic size uncertainties shows that for the majority of over 100,000 objects with WISE-based size estimates random uncertainties (precision) are about 10%; systematic uncertainties within the adopted model framework, such as NEATM, and with assumed emissivity for WISE W3 and W4 bands, are likely in the range of 10–20%. Hence, the accuracy of WISE-based asteroid size estimates is approximately in the range of 15–20% for most objects, except for unknown errors due to a possibly over-simplified modeling framework (e.g., spherical asteroid approximation). We compare model families to data in WISE color-color diagrams and derive a simple method to estimate size that only uses WISE W3 band flux; we show that it matches estimates based on all four WISE bands to within 10%. 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 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 17% 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.
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