  Stellar blends, where two or more stars appear blended in an image, pose a
significant visualization challenge in astronomy. Traditionally, distinguishing
these blends from single stars has been costly and resource-intensive,
involving sophisticated equipment and extensive expert analysis. This is
especially problematic for analyzing the vast data volumes from surveys, such
as Legacy Survey of Space and Time (LSST), Sloan Digital Sky Survey (SDSS),
Dark Energy Spectroscopic Instrument (DESI), Legacy Imaging Survey and the
Zwicky Transient Facility (ZTF). To address these challenges, we apply
different normalizations and data embeddings on low resolution images of single
stars and stellar blends, which are passed as inputs into machine learning
methods and to a computationally efficient Gaussian process model (MuyGPs).
MuyGPs consistently outperforms the benchmarked models, particularly on limited
training data. Moreover, MuyGPs with $r^\text{th}$ root local min-max
normalization achieves 83.8% accuracy. Furthermore, MuyGPs' ability to produce
confidence bands ensures that predictions with low confidence can be redirected
to a specialist for efficient human-assisted labeling.
