Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

@article{Cheng2020IdentifyingSL,
  title={Identifying strong lenses with unsupervised machine learning using convolutional autoencoder},
  author={Ting-Yun Cheng and Nan Li and Christopher J. Conselice and Alfonso Arag'on-Salamanca and Simon Dye and R. Benton Metcalf},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2020}
}
In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii… 

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