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Published June 1, 2018 | Published
Journal Article Open

Effective image differencing with convolutional neural networks for real-time transient hunting


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 point-spread function (PSF) and small brightness variations in many sources, as well as artefacts 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 artefacts. 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 – in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night.

Additional Information

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2018 February 25. Received 2018 February 19; in original form 2017 October 5. Published: 10 April 2018. AM was supported in part by the NSF grants AST-0909182, AST-1313422, AST-1413600 and AST-1518308, and by the Ajax Foundation.

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