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Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting

Pearson, James; Maresca, Jacob; Li, Nan; Dye, Simon

Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting Thumbnail


Authors

James Pearson

Jacob Maresca

Nan Li

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



Abstract

The vast quantity of strong galaxy–galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling for a range of increasingly complex lensing systems. These include standard smooth parametric density profiles, hydrodynamical EAGLE galaxies, and the inclusion of foreground mass structures, combined with parametric sources and sources extracted from the Hubble Ultra Deep Field. In addition, we also present a method for combining the CNN with traditional parametric density profile fitting in an automated fashion, where the CNN provides initial priors on the latter’s parameters. On average, the CNN achieved errors 19 ± 22 per cent lower than the traditional method’s blind modelling. The combination method instead achieved 27 ± 11 per cent lower errors over the blind modelling, reduced further to 37 ± 11 per cent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 ± 21 per cent lower than the CNN by itself. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional fitting alone by factors of 1.73 and 1.19 with and without CNN-predicted uncertainties, respectively. This, combined with greatly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.

Journal Article Type Article
Acceptance Date May 22, 2021
Online Publication Date May 28, 2021
Publication Date 2021-08
Deposit Date Jun 4, 2021
Publicly Available Date Jun 18, 2021
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
Volume 505
Issue 3
Pages 4362-4382
DOI https://doi.org/10.1093/mnras/stab1547
Keywords Space and Planetary Science; Astronomy and Astrophysics
Public URL https://nottingham-repository.worktribe.com/output/5620441
Publisher URL https://academic.oup.com/mnras/article/505/3/4362/6287584?login=true
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.

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