Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images

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.

9 pages, 6 figures, MNRAS accepted

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• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
• Dark Matter Subhalos, Strong Lensing and Machine Learning
• Construction of a far ultraviolet all sky map from an incomplete survey: Application of a deep learning algorithm