$\texttt{DiffLense}$: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data

TL;DR

DiffLense introduces a conditional diffusion model that significantly enhances the resolution of gravitational lensing images, preserving fine details crucial for astrophysical research, by leveraging HST data as a reference.

Contribution

This work presents a novel diffusion-based super-resolution pipeline tailored for gravitational lensing images, outperforming existing methods in detail preservation and noise reduction.

Findings

Outperforms state-of-the-art super-resolution techniques

Preserves fine astrophysical details better than previous methods

Reduces noise and background interference effectively

Abstract

Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce $\texttt{DiffLense}$, a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble Space Telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model's training phase. We demonstrate that $\texttt{DiffLense}$ outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.