Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

TL;DR

This paper introduces two convolutional neural networks that significantly reduce false positives and automate classification in optical difference imaging for multimessenger event follow-up, speeding up counterpart identification.

Contribution

The authors develop and validate CNN-based methods to improve false positive removal and source classification in difference imaging, reducing human inspection needs.

Findings

False detection accuracy of 92% with CNN

Reduces human inspection by 1.5 times alone

Reduces human inspection by 3.6 times with existing algorithms

Abstract

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor quality image subtractions, false detections from low signal-to-noise images, and detections of pre-existing variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92 percent on our dataset. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.