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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.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1093/mnras/stz1288
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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.

Item Type: Journal Item
Copyright Holders: 2019 The Authors
ISSN: 1365-2966
Keywords: Space and Planetary Science; Astronomy and Astrophysics
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Physical Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Item ID: 61489
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 29 May 2019 08:52
Last Modified: 25 Jan 2020 13:13
URI: http://oro.open.ac.uk/id/eprint/61489
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