  Accurately detecting rendezvous and proximity operations (RPO) is crucial for
understanding how objects are behaving in the space domain. However, detecting
closely-spaced objects (CSO) is challenging for ground-based optical space
domain awareness (SDA) algorithms as two objects close together along the
line-of-sight can appear blended as a single object within the point-spread
function (PSF) of the optical system. Traditional machine learning methods can
be useful for differentiating between singular objects and closely-spaced
objects, but many methods require large training sample sizes or high
signal-to-noise conditions. The quality and quantity of realistic data make
probabilistic classification methods a superior approach, as they are better
suited to handle these data inadequacies. We present CSO classification results
using the Gaussian process python package, MuyGPyS, and examine classification
accuracy as a function of angular separation and magnitude difference between
the simulated satellites. This orbit-independent analysis is done on highly
accurate simulated SDA images that emulate realistic ground-based
commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find
that MuyGPyS outperforms traditional machine learning methods, especially under
more challenging circumstances.
