Title: Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks,
Abstract: Quantifying image distortions caused by strong gravitational lensing and
estimating the corresponding matter distribution in lensing galaxies has been
primarily performed by maximum likelihood modeling of observations. This is
typically a time and resource-consuming procedure, requiring sophisticated
lensing codes, several data preparation steps, and finding the maximum
likelihood model parameters in a computationally expensive process with
downhill optimizers. Accurate analysis of a single lens can take up to a few
weeks and requires the attention of dedicated experts. Tens of thousands of new
lenses are expected to be discovered with the upcoming generation of ground and
space surveys, the analysis of which can be a challenging task. Here we report
the use of deep convolutional neural networks to accurately estimate lensing
parameters in an extremely fast and automated way, circumventing the
difficulties faced by maximum likelihood methods. We also show that lens
removal can be made fast and automated using Independent Component Analysis of
multi-filter imaging data. Our networks can recover the parameters of the
Singular Isothermal Ellipsoid density profile, commonly used to model strong
lensing systems, with an accuracy comparable to the uncertainties of
sophisticated models, but about ten million times faster: 100 systems in
approximately 1s on a single graphics processing unit. These networks can
provide a way for non-experts to obtain lensing parameter estimates for large
samples of data. Our results suggest that neural networks can be a powerful and
fast alternative to maximum likelihood procedures commonly used in
astrophysics, radically transforming the traditional methods of data reduction
and analysis.