  The rise of synoptic sky surveys has ushered in an era of big data in
time-domain astronomy, making data science and machine learning essential tools
for studying celestial objects. While tree-based models (e.g. Random Forests)
and deep learning models dominate the field, we explore the use of different
distance metrics to aid in the classification of astrophysical objects. We
developed DistClassiPy, a new distance metric based classifier. The direct use
of distance metrics is unexplored in time-domain astronomy, but distance-based
methods can help make classification more interpretable and decrease
computational costs. In particular, we applied DistClassiPy to classify light
curves of variable stars, comparing the distances between objects of different
classes. Using 18 distance metrics on a catalog of 6,000 variable stars across
10 classes, we demonstrate classification and dimensionality reduction. Our
classifier meets state-of-the-art performance but has lower computational
requirements and improved interpretability. Additionally, DistClassiPy can be
tailored to specific objects by identifying the most effective distance metric
for that classification. To facilitate broader applications within and beyond
astronomy, we have made DistClassiPy open-source and available at
https://pypi.org/project/distclassipy/.
