Probabilistic modeling of asteroid diameters from Gaia DR2 errors

TL;DR

This paper develops a probabilistic model linking Gaia DR2 astrometric errors to asteroid diameters, enabling identification of large asteroids for targeted direct measurement and shape analysis.

Contribution

It introduces a Bayesian additive regression tree model to relate Gaia errors to asteroid sizes, aiding in selecting candidates for detailed study.

Findings

Large asteroids (>20 km) show high correlation with Gaia flux uncertainties.

The model successfully identifies probable large asteroids for follow-up observations.

Provides a list of candidate asteroids for direct diameter measurement.

Abstract

The Gaia Data Release 2 provides precise astrometry for nearly 1.5 billion sources across the entire sky, including several thousand asteroids. In this work, we provide evidence that reasonably large asteroids (diameter $>$ 20 km) have high correlations with Gaia relative flux uncertainties and systematic right ascension errors. We further capture these correlations using a logistic Bayesian additive regression tree model. We compile a small list of probable large asteroids that can be targeted for direct diameter measurements and shape reconstruction.