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

TL;DR

This paper compares Bayesian neural networks and parametric profile fitting for automated strong lens modelling, demonstrating that combining both methods improves accuracy and efficiency over traditional approaches.

Contribution

It introduces a method to combine CNN-based predictions with parametric fitting, enhancing accuracy and speed in modelling complex gravitational lenses.

Findings

CNN reduces errors by 19% compared to traditional methods

Combining CNN with parametric fitting reduces errors by 27%

The combined approach increases modelling speed by up to 1.73 times

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 $\pm$ 22 per cent lower than the traditional method's blind modelling. The combination method instead achieved 27 $\pm$ 11 per cent lower errors over the blind modelling, reduced further to 37 $\pm$ 11 per cent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 $\pm$ 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.