Finding strong lenses in CFHTLS using convolutional neural networks

  title={Finding strong lenses in CFHTLS using convolutional neural networks},
  author={Colin Jacobs and Karl Glazebrook and Thomas E. Collett and Anupreeta More and Christopher Mccarthy},
  journal={Monthly Notices of the Royal Astronomical Society},
We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62,406 simulated lenses and 64,673 non-lens negative examples generated with two different… Expand
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