  We present a method for characterizing image-subtracted objects based on
shapelet analysis to identify transient events in ground-based time-domain
surveys. We decompose the image-subtracted objects onto a set of discrete
Zernike polynomials and use their resulting coefficients to compare them to
other point-like objects. We derive a norm in this Zernike space that we use to
score transients for their point-like nature and show that it is a powerful
comparator for distinguishing image artifacts, or residuals, from true
astrophysical transients. Our method allows for a fast and automated way of
scanning overcrowded, wide-field telescope images with minimal human
interaction and we reduce the large set of unresolved artifacts left
unidentified in subtracted observational images. We evaluate the performance of
our method using archival intermediate Palomar Transient Factory and Dark
Energy Camera survey images. However, our technique allows flexible
implementation for a variety of different instruments and data sets. This
technique shows a reduction in image subtraction artifacts by 99.95% for
surveys extending up to hundreds of square degrees and has strong potential for
automated transient identification in electromagnetic follow-up programs
triggered by the Laser Interferometer Gravitational Wave Observatory-Virgo
Scientific Collaboration.
