Robust Filtering of Artifacts in Difference Imaging for Rapid Transients Detection

TL;DR

This paper introduces a hierarchical unsupervised classifier using Self-Organizing Maps to effectively filter artifacts in difference imaging, enabling rapid transient detection in sky surveys.

Contribution

A novel artifact filtering method employing SOMs that significantly improves real transient retention and artifact removal, facilitating faster alerts in sky surveys.

Findings

97% of real transients accepted

97.5% of artifacts removed

Transient alerts issued within 15 minutes

Abstract

Real-time analysis and classification of observational data collected within synoptic sky surveys is a huge challenge due to constant growth of data volumes. Machine learning techniques are often applied in order to perform this task automatically. The current bottleneck of transients detection in most surveys is the process of filtering numerous artifacts from candidate detection. We present a new method for automated artifact filtering based on hierarchical unsupervised classifier employing Self-Organizing Maps (SOMs). The system accepts 97 % of real transients and removes 97.5 % of artifacts when tested on the OGLE-IV Transient Detection System. The improvement of the artifacts filtering allowed for single-frame based rapid detections of transients within OGLE-IV, which now alerts on transient discoveries in less than 15 minutes from the image acquisition.