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Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder (2020)
Journal Article
Cheng, T., Li, N., Conselice, C. J., Aragón-Salamanca, A., Dye, S., & Metcalf, R. B. (2020). Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder. Monthly Notices of the Royal Astronomical Society, 394(3), 3750–3765. https://doi.org/10.1093/mnras/staa1015

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 v... Read More about Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder.

Euclid: the selection of quiescent and star-forming galaxies using observed colours (2020)
Journal Article
Bisigello, L., Kuchner, U., Conselice, C., Andreon, S., Bolzonella, M., Duc, P., …Zoubian, J. (2020). Euclid: the selection of quiescent and star-forming galaxies using observed colours. Monthly Notices of the Royal Astronomical Society, 494(2), 2337-2354. https://doi.org/10.1093/mnras/staa885

The Euclid mission will observe well over a billion galaxies out to z ∼ 6 and beyond. This will offer an unrivalled opportunity to investigate several key questions for understanding galaxy formation and evolution. The first step for many of these st... Read More about Euclid: the selection of quiescent and star-forming galaxies using observed colours.

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging (2020)
Journal Article
Cheng, T., Conselice, C. J., Aragón-Salamanca, A., Li, N., Bluck, A. F., Hartley, W. G., …Tarle, G. (2020). Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. Monthly Notices of the Royal Astronomical Society, 493(3), 4209-4228. https://doi.org/10.1093/mnras/staa501

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation... Read More about Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging.