Cataloging and Characterizing the Small Bodies of the Solar System with LSST

Astronomy and Astrophysics – Astronomy

Scientific paper

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Scientific paper

LSST will address key planetary science objectives identified as priorities by diverse national science panels. This is due to LSST's Deep-Wide-Fast survey implementation, which is ideal for detecting faint solar system objects. Specifically, the proposed cadence includes two back-to-back 15 second exposures that will detect sources as faint as r=24.7. By revisiting each 9.6 square degree field within roughly 30 minutes, the system will find about 4000 moving objects per field near the ecliptic and cover the entire observable sky 4-5 times per lunation. LSST will produce an instantly public multicolor dataset of exceptional accuracy, with, for example, 50 mas astrometry and 70 millimag photometry at r=23.5.
The ten-year survey will discover approximately 15,000 Potentially Hazardous Asteroids larger than 140m, approaching the George Brown Near-Earth Object Survey goal set by Congress of cataloging 90% of such objects. It will also find millions of main belt asteroids and up to 100,000 transneptunian objects, as well as thousands of objects in between, such as comets, Trojans, Centaurs and irregular satellites of the outer planets.
The orbit, size and color distribution derived for various classes of object will allow new tests and constraints for theories on the formation and evolution of the solar system. Detection of very distant objects will permit new insights into the nature of the Oort cloud. The sparse photometry from LSST detections will be sufficient to enable spin state and shape models to be developed for tens of thousands of asteroids. By revealing perturbations on smaller asteroids, the accurate astrometry will allow estimates of the mass of hundreds of main belt asteroids. By pushing to ever smaller sizes, derivation of proper elements for main belt asteroids will greatly enlarge existing asteroid families, and precise multi-color photometry will facilitate further division by correlating color with dynamical class and history.

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