Predictions of the LSST Solar System Yield: Near-Earth Objects, Main Belt Asteroids, Jupiter Trojans, and Trans-Neptunian Objects

TL;DR

The paper presents a high-fidelity simulation of LSST's solar system catalog, predicting the discovery of over a million small bodies and providing detailed data for early and future solar system science.

Contribution

It introduces the first comprehensive simulation of LSST's solar system observations, estimating the number of known objects and their measurable properties across various small body populations.

Findings

Over 1.1 billion observations of small bodies predicted

Number of known objects will increase 4-9 times across classes

70% of main belt and distant objects discovered within two years

Abstract

The NSF-DOE Vera C. Rubin Observatory is a new 8m-class survey facility presently being commissioned in Chile, expected to begin the 10yr-long Legacy Survey of Space and Time (LSST) by the end of 2025. Using the purpose-built Sorcha survey simulator (Merritt et al. In Press), and near-final observing cadence, we perform the first high-fidelity simulation of LSST's solar system catalog for key small body populations. We show that the final LSST catalog will deliver over 1.1 billion observations of small bodies and raise the number of known objects to 1.27E5 near-Earth objects, 5.09E6 main belt asteroids, 1.09E5 Jupiter Trojans, and 3.70E4 trans-Neptunian objects. These represent 4-9x more objects than are presently known in each class, making LSST the largest source of data for small body science in this and the following decade. We characterize the measurements available for these populations, including orbits, griz colors, and lightcurves, and point out science opportunities they open. Importantly, we show that ~70% of the main asteroid belt and more distant populations will be discovered in the first two years of the survey, making high-impact solar system science possible from very early on. We make our simulated LSST catalog publicly available, allowing researchers to test their methods on an up-to-date, representative, full-scale simulation of LSST data.