Effective Image Differencing with ConvNets for Real-time Transient Hunting

Understand

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.