Effective Image Differencing with ConvNets for Real-time Transient Hunting

TL;DR

This paper introduces a deep learning method that consolidates all steps of traditional image subtraction into a single, fast convolutional network for real-time transient detection in sky surveys.

Contribution

It presents a novel deep-learning approach that integrates image registration, background subtraction, noise removal, PSF matching, and subtraction into one efficient model.

Findings

Works in real-time for large sky surveys

Handles varying PSF and artifacts effectively

Speeds up transient detection process

Abstract

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as artifacts resulting from saturated stars, and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artifacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image subtraction pipeline -- image registration, background subtraction, noise removal, psf matching, and subtraction -- into a single real-time convolutional network. Once trained the method works lighteningly fast, and given that it does multiple steps at one go, the advantages for multi-CCD, fast surveys like ZTF and LSST are obvious.