Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder

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

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.

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

• Journal Article Type: Article
• Acceptance Date: Apr 8, 2020
• Online Publication Date: Apr 17, 2020
• Publication Date: 2020-05
• Deposit Date: Apr 25, 2020
• Publicly Available Date: Apr 27, 2020
• 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
• DOI: https://doi.org/10.1093/mnras/staa1015
• Keywords: Space and Planetary Science; Astronomy and Astrophysics
• Public URL: https://nottingham-repository.worktribe.com/output/4341228
• 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.