Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images

Davies, Andrew; Serjeant, Stephen and Bromley, Jane M. (2019). Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images. Monthly Notices of the Royal Astronomical Society, 487(4) pp. 5263–5271.

DOI: https://doi.org/10.1093/mnras/stz1288

Abstract

The Euclid telescope, due for launch in 2021, will perform an imaging and slitless spectroscopy survey over half the sky, to map baryon wiggles and weak lensing. During the survey Euclid is expected to resolve 100,000 strong gravitational lens systems. This is ideal to find rare lens configurations, provided they can be identified reliably and on a reasonable timescale. For this reason we have developed a Convolutional Neural Network (CNN) that can be used to identify images containing lensing systems. CNNs have already been used for image and digit classification as well as being used in astronomy for star-galaxy classification. Here our CNN is trained and tested on Euclid-like and KiDS-like simulations from the Euclid Strong Lensing Group, successfully classifying 77% of lenses, with an area under the ROC curve of up to 0.96. Our CNN also attempts to classify the lenses in COSMOS HST F814W-band images. After convolution to the Euclid resolution, we find we can recover most systems that are identifiable by eye. The Python code is available on Github.

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