  Uncorrelated optical space observation association represents a classic
needle in a haystack problem. The objective being to find small groups of
observations that are likely of the same resident space objects (RSOs) from
amongst the much larger population of all uncorrelated observations. These
observations being potentially widely disparate both temporally and with
respect to the observing sensor position. By training on a large representative
data set this paper shows that a deep learning enabled learned model with no
encoded knowledge of physics or orbital mechanics can learn a model for
identifying observations of common objects. When presented with balanced input
sets of 50% matching observation pairs the learned model was able to correctly
identify if the observation pairs were of the same RSO 83.1% of the time. The
resulting learned model is then used in conjunction with a search algorithm on
an unbalanced demonstration set of 1,000 disparate simulated uncorrelated
observations and is shown to be able to successfully identify true three
observation sets representing 111 out of 142 objects in the population. With
most objects being identified in multiple three observation triplets. This is
accomplished while only exploring 0.06% of the search space of 1.66e8 possible
unique triplet combinations.
