The Vera C. Rubin Observatory's proposed Legacy Survey of Space and Time (LSST) is expected to vastly expand our knowledge of moving objects present in the Solar System. Roughly 60 petabytes of astronomical data will be produced over the 10 year campaign, with which several million Solar System Objects are expected to be discovered. The new data analysis pipelines developed to handle the massive throughput generated by the LSST require testing on simulated datasets to ensure robustness and evaluate performance. I present a survey simulator that I have codeveloped with colleagues at the NASA Jet Propulsion Laboratory. The software can ingest real or modeled orbits of Solar System Objects and produces a corresponding catalog with billions of detections. Running the survey simulator on objects currently known as well as synthetic models of the Solar System, I have created a dataset that will play a vital role in the testing of LSST pipelines and create more extensive estimates of discovery rates for the various minor planet populations of the Solar System in order to confirm current expectations.

Astronomy of the 21st century is driven by large data sets collected by large automated sky surveys. The largest survey project currently being built is the Legacy Survey of Space and Time (LSST), which will be a 10-year survey of the southern sky, expected to discover 5.5 million small bodies in our Solar System. The greater scientific community needs to know what the research potential and scope of the data LSST will collect. I am building a database -- accessible at http://ls.st/ssdb -- of simulated LSST observations of asteroids in our Solar System. My work delves into simulation accuracy, big data analysis, and database management. This dataset consists of individual observations, an orbit catalog, and a catalog of physical and observational characteristics. Using simulations from the University of Washington’s Data Intensive Research in Astrophysics and Cosmology Institute (DiRAC) and scripting in Python, I am attempting to accurately present this data and areas of possible research before LSST becomes operational. This project will be integral to preparing for research projects that will analyze actual LSST data.