Using neural networks to model Main Belt Asteroid albedos as a function
  of their proper orbital elements
Zachary Murray
Asteroid diameters are traditionally difficult to estimate. When a direct
measurement of the diameter cannot be made through either occultation or direct
radar observation, the most common method is to approximate the diameter from
infrared observations. Once the diameter is known, a comparison with visible
light observations can be used to find the visible geometric albedo of the
body. One of the largest datasets of asteroid albedos comes from the NEOWISE
mission, which measured asteroid albedos both in the visible and infrared. We
model these albedos as a function of proper elements available from the
Asteroid Families Portal using an ensemble of neural networks. We find that
both the visible and infrared geometric albedos are significantly correlated
with asteroid position in the belt and occur in both asteroid families and in
the background belt. We find that the ensemble's prediction reduces the average
error in albedo by about 37% compared to a model that simply adopts an average
albedo, with no regard for the dynamical state of the body. We then use this
model to predict albedos for the half million main belt asteroids with proper
elements available in the Asteroid Families Portal and provide the results in a
catalog. Finally, we show that several presently categorized asteroid families
exist within much larger groups of asteroids of similar albedos - this may
suggest that further improvements in family identification can be made.