Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

ABSTRACT 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 t...

Full description

Saved in:
Bibliographic Details
Published in:Monthly notices of the Royal Astronomical Society Vol. 494; no. 3; pp. 3750 - 3765
Main Authors: Cheng, Ting-Yun, Li, Nan, Conselice, Christopher J, Aragón-Salamanca, Alfonso, Dye, Simon, Metcalf, Robert B
Format: Journal Article
Language:English
Published: Oxford University Press 21.05.2020
Subjects:
techniques: image processing
methods: statistical
gravitational lensing: strong
ISSN:0035-8711, 1365-2966
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:ABSTRACT 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, 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 per cent 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 per cent 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.
ISSN:0035-8711
1365-2966
DOI:10.1093/mnras/staa1015