Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks

TL;DR

This paper demonstrates that deep convolutional neural networks can rapidly and accurately estimate strong gravitational lensing parameters, significantly outperforming traditional methods in speed and automation, enabling analysis of large datasets.

Contribution

The authors introduce a neural network-based approach that automates and accelerates the analysis of gravitational lenses, achieving comparable accuracy to traditional models but at a fraction of the computational cost.

Findings

Neural networks recover lensing parameters with accuracy similar to traditional models.

Parameter estimation is about ten million times faster than maximum likelihood methods.

The approach enables rapid analysis of large lensing datasets, facilitating non-expert use.

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.