Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

TL;DR

This paper introduces a deep learning method to analyze gravitational lens images, quantifying substructure in galaxy mass distributions with uncertainty estimates, without explicit lens modeling, and validated on simulated and real data.

Contribution

The novel neural network handles uncertainty intervals, provides probability distributions, and accurately estimates lens perturbation parameters directly from images without traditional modeling.

Findings

Accurately estimates perturbation parameters from lens images.

Reduces confidence interval widths by 10% without ground truth.

Robustly quantifies lens mass smoothness across many cases.

Abstract

Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training dataset labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e., without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.