Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models

TL;DR

This paper introduces a novel method for galaxy source reconstruction in strong lensing systems using denoising diffusion probabilistic models, providing data-driven priors that improve the fidelity and variability of reconstructions.

Contribution

The authors develop a diffusion-based prior for galaxy sources in lensing analysis, leveraging pre-trained generative models to enhance reconstruction accuracy and uncertainty quantification.

Findings

Reconstructed sources closely resemble observations in low-noise scenarios.

Reconstructions from uncertain data exhibit greater variability, reflecting model uncertainty.

The method effectively encodes the distribution of galaxy images as a prior.

Abstract

Analysis of galaxy--galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior / regularisation for source galaxies based on denoising diffusion probabilistic models (DDPMs). We use a pre-trained model for galaxy images, AstroDDPM, and a chain of conditional reconstruction steps called denoising diffusion reconstruction model (DDRM) to obtain samples consistent both with the noisy observation and with the distribution of training data for AstroDDPM. We show that these samples have the qualitative properties associated with the posterior for the source model: in a low-to-medium noise scenario they closely resemble the observation, while reconstructions from uncertain data show greater variability, consistent with the distribution encoded in the generative model used as prior.