Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

Maresca, Jacob; Dye, Simon; Li, Nan

Abstract

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy–galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a convolutional neural network (CNN) that analyses the outputs of semi-analytic methods that parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialized lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically reinitialize the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sérsic sources, accurately classifies source reconstructions of the same type with a precision P > 0.99 and recall R > 0.99. The same CNN, without retraining, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed Hubble Ultra-Deep Field (HUDF) sources. Using the CNN predictions to reinitialize the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.

Citation

Maresca, J., Dye, S., & Li, N. (2021). Auto-identification of unphysical source reconstructions in strong gravitational lens modelling. Monthly Notices of the Royal Astronomical Society, 503(2), 2229–2241. https://doi.org/10.1093/mnras/stab387

• Journal Article Type: Article
• Acceptance Date: Feb 5, 2021
• Online Publication Date: Feb 11, 2021
• Publication Date: 2021-05
• Deposit Date: Feb 16, 2021
• Publicly Available Date: Apr 27, 2021
• 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: 503
• Issue: 2
• Pages: 2229–2241
• DOI: https://doi.org/10.1093/mnras/stab387
• Keywords: Space and Planetary Science; Astronomy and Astrophysics
• Public URL: https://nottingham-repository.worktribe.com/output/5319962
• Publisher URL: https://academic.oup.com/mnras/article/503/2/2229/6133451
• Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2021 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.