Using Dust Shed from Asteroids as Microsamples to Link Remote Measurements with Meteorite Classes

TL;DR

This paper proposes using dust shed from asteroids as microsamples to connect remote sensing data with meteorite classifications, enabling asteroid characterization without sample return.

Contribution

It introduces models and Monte Carlo simulations to estimate dust particle detection around asteroids, linking in situ measurements to meteorite classes.

Findings

Detection of microsamples can classify asteroid types.

Several tens to hundreds of particles suffice for classification.

Dust detection models depend on asteroid size and flyby parameters.

Abstract

Given the compositional diversity of asteroids, and their distribution in space, it is impossible to consider returning samples from each one to establish their origin. However, the velocity and molecular composition of primary minerals, hydrated silicates, and organic materials can be determined by in situ dust detector instruments. Such instruments could sample the cloud of micrometer-scale particles shed by asteroids to provide direct links to known meteorite groups without returning the samples to terrestrial laboratories. We extend models of the measured lunar dust cloud from LADEE to show that the abundance of detectable impact-generated microsamples around asteroids is a function of the parent body radius, heliocentric distance, flyby distance, and speed. We use monte carlo modeling to show that several tens to hundreds of particles, if randomly ejected and detected during a flyby, would be a sufficient number to classify the parent body as an ordinary chondrite, basaltic achondrite, or other class of meteorite. Encountering and measuring microsamples shed from near-earth and main-belt asteroids, coupled with complementary imaging and multispectral measurements, could accomplish a thorough characterization of small, airless bodies.