  We introduce a novel method for discerning optical telescope images of stars
from those of galaxies using Gaussian processes (GPs). Although applications of
GPs often struggle in high-dimensional data modalities such as optical image
classification, we show that a low-dimensional embedding of images into a
metric space defined by the principal components of the data suffices to
produce high-quality predictions from real large-scale survey data. We develop
a novel method of GP classification hyperparameter training that scales
approximately linearly in the number of image observations, which allows for
application of GP models to large-size Hyper Suprime-Cam (HSC) Subaru Strategic
Program data. In our experiments we evaluate the performance of a principal
component analysis (PCA) embedded GP predictive model against other machine
learning algorithms including a convolutional neural network and an image
photometric morphology discriminator. Our analysis shows that our methods
compare favorably with current methods in optical image classification while
producing posterior distributions from the GP regression that can be used to
quantify object classification uncertainty. We further describe how
classification uncertainty can be used to efficiently parse large-scale survey
imaging data to produce high-confidence object catalogs.
