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Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder

Cheng, Ting-Yun; Li, Nan; Conselice, Christopher J; Aragón-Salamanca, Alfonso; Dye, Simon; Metcalf, Robert B

Authors

Ting-Yun Cheng

Nan Li

Christopher J Conselice

SIMON DYE Simon.Dye@nottingham.ac.uk
Associate Professor

Robert B Metcalf



Abstract

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 radii, distorted arc structures, etc, without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up ∼63 percent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 ± 0.48% in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique.

Journal Article Type Article
Publication Date 2020-05
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 394
Issue 3
Pages 3750–3765
APA6 Citation 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
DOI https://doi.org/10.1093/mnras/staa1015
Keywords Space and Planetary Science; Astronomy and Astrophysics
Publisher URL https://academic.oup.com/mnras/advance-article-abstract/doi/10.1093/mnras/staa1015/5821287?redirectedFrom=fulltext
Additional Information This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record Ting-Yun Cheng, Nan Li, Christopher J Conselice, Alfonso Aragón-Salamanca, Simon Dye, Robert B Metcalf, Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder, Monthly Notices of the Royal Astronomical Society, staa1015 is available online at: https://doi.org/10.1093/mnras/staa1015.

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