The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

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We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters.

• We have investigated a number of practicalities faced when modelling real image data, such as how network performance depends on the inclusion of lens galaxy light, the addition of colour information and varying signal-to-noise.
• Our CNN was trained and tested with strong galaxy-galaxy lens images simulated to match the imaging characteristics of the Large Synoptic Survey Telescope (LSST) and Euclid.
• For images including lens galaxy light, the CNN can recover the lens model parameters with an acceptable accuracy, although a 34 per cent average improvement in accuracy is obtained when lens light is removed.