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.
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- May 2021
- gravitational lensing: strong;
- galaxies: structure;
- Astrophysics - Astrophysics of Galaxies
- 13 pages, 13 figures, accepted for publication in MNRAS