Automated Transient Detection with Shapelet Analysis in Image-subtracted Data

Abstract: 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.