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The Large Synoptic Survey Telescope (LSST) is expected to increase known small solar system object populations by an order of magnitude or more over the next decade, enabling a broad array of transformative solar system science investigations to be performed. In this white paper, we discuss software tools and infrastructure that we anticipate will be needed to conduct these investigations and outline possible approaches for implementing them. Feedback from the community or contributions to future updates of this work are welcome. Our aim is for this white paper to encourage further consideration of the software development needs of the LSST solar system science community, and also to be a call to action for working to meet those needs in advance of the expected start of the survey in late 2022., 82 pages

A foundational goal of the Large Synoptic Survey Telescope (LSST) is to map the Solar System small body populations that provide key windows into understanding of its formation and evolution. This is especially true of the populations of the Outer Solar System -- objects at the orbit of Neptune $r > 30$AU and beyond. In this whitepaper, we propose a minimal change to the LSST cadence that can greatly enhance LSST's ability to discover faint distant Solar System objects across the entire wide-fast-deep (WFD) survey area. Specifically, we propose that the WFD cadence be constrained so as to deliver least one sequence of $\gtrsim 10$ visits per year taken in a $\sim 10$ day period in any combination of $g, r$, and $i$ bands. Combined with advanced shift-and-stack algorithms (Whidden et al. 2019) this modification would enable a nearly complete census of the outer Solar System to $\sim 25.5$ magnitude, yielding $4-8$x more KBO discoveries than with single-epoch baseline, and enabling rapid identification and follow-up of unusual distant Solar System objects in $\gtrsim 5$x greater volume of space. These increases would enhance the science cases discussed in Schwamb et al. (2018) whitepaper, including probing Neptune's past migration history as well as discovering hypothesized planet(s) beyond the orbit of Neptune (or at least placing significant constraints on their existence)., White Paper submitted in response to the Call for LSST Cadence Optimization White Papers

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