Automated Transient Detection with Shapelet Analysis in Image-subtracted Data
Kendall Ackley, Stephen S. Eikenberry, Ceren Yildirim, Sergey, Klimenko, Alan Garner

TL;DR
This paper introduces a shapelet-based method using Zernike polynomial coefficients to efficiently distinguish true astrophysical transients from artifacts in wide-field, image-subtracted astronomical data, enabling rapid automated detection.
Contribution
The novel approach applies Zernike polynomial analysis to classify transients, significantly reducing artifacts and improving automation in transient detection in large-scale surveys.
Findings
99.95% reduction in subtraction artifacts
Effective on multiple survey datasets
Potential for real-time gravitational wave follow-up
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…
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