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Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
[article]
2020
arXiv
pre-print
In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). 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
arXiv:1911.04320v2
fatcat:6mzk2sppqzexhfi2nwc55pisxu