Malhotra will provide expertise on dynamical analyses and algorithm development on this project. Specifically, she will advise on the design of the proper element calculation algorithm, advise on dynamical classifications and characterizations, and help disseminate results of this project. Project Motivation: We will soon enter a new era of solar system small body science when LSST increases the number of known solar system small bodies by over an order of magnitude. Dynamical analyses of small bodies have led to many important insights in planetary science. The dynamical evolution of an observed small body’s orbit can place it into context as, for example, a likely primordial small body whose orbit has remained largely unchanged since formation (like the New Horizon’s target Arrokoth); similarly, dynamical evolution marked by rapid changes can indicate an object belongs to a short-lived, transient class of objects such as the giant-planet crossing Centaur population. Calculation of proper orbital elements can reveal groupings of objects, such as collisional families. Identification of objects in mean motion resonances (MMRs) can help test models of the solar system’s early dynamical history. The community needs user-friendly, accessible tools for dynamical analyses to fully exploit the upcoming quantity and quality of small body observations and enable transformative scientific progress in many areas of planetary science. Project Goals and Objectives: We will develop a well-documented, open-source Python package, Small Body Dynamics Tool (SBDynT) that takes a small body orbit (from observed or modeled populations), performs dynamical integrations of its orbital evolution, calculates a variety of dynamical parameters, and outputs dynamical characterizations and classifications. Example outputs of SBDynT include proper orbital elements, chaos indicators, whether an object is in a planetary mean motion resonance, dynamical classification according to commonly used schemes, dynamical lifetime estimates, and characterization of orbital changes over different timescales, including past orbital history. Methods and Approach: We will leverage our team’s prior experience with dynamical characterization of both simulated and observed small bodies as well as already available opensource tools such as the Rebound orbit integration package to build SBDynT. We will test and validate different approaches for dynamical characterization and classification (e.g., frequency analysis, machine learning) and we will incorporate the ability to characterize an observed small body’s orbit using clones to account for observational uncertainties. While generalizable to any small body (observed or modeled), SBDynT will be designed and extensively tested for bodies whose evolution is dominated by gravitational effects (i.e, the main asteroid belt, Trojans, Centaurs, TNOs, etc). Significance: SBDynT will enable the small body research community to quickly and easily characterize an object based on dynamical behavior; this will be particularly impactful for non- dynamicists who might want to prioritize follow-up observations of objects based on orbital evolution characteristics. For dynamicists, this tool will provide a common platform for both numerical simulations and observed objects, enabling direct and self-consistent comparisons of models to observations. SBDynT will also minimize future duplication of effort by leveraging our expertise to provide a set of dynamical characterization tools to perform common tasks. Relevance: This proposed effort is relevant to PDART because the goal is to “develop and disseminate software tools that facilitate the use of existing datasets or that would enable or enhance future science investigations of interest to the Planetary Science Division”. In particular, SBDynT would fulfill the need identified by Schwamb et al. 2019 (arXiv:1802.01783) and Hsieh et al. 2020 (arXiv:1906.11346) to provide dynamical characterization and resonance identification for objects discovered by LSST. It would also enhance the science return of many past and current surveys by providing a consistent dynamical characterization framework for the already existing small body dataset.