Joint multiband deconvolution for Euclid and Vera C. Rubin images

TL;DR

This paper tackles the challenge of obtaining high-resolution, multiband astronomical images by jointly deconvolving ground-based Rubin $r,i,z$ data with space-based Euclid VIS data, exploiting overlapping filter coverage. The authors formulate a forward model and a variational objective that couples per-band data fidelity with a flux-consistency constraint across bands, implemented via gradient-descent optimization and a shared Euclid term $K$. They demonstrate the approach on realistic HST-based GOODS cutouts, generating Rubin and Euclid simulations with appropriate PSFs and noise, achieving resolution near that of HST and preserved flux, with a denoising stage using DRUNet improving background suppression. A derivative using Plug-and-Play ADMM with DRUNet shows only marginal NMSE gains but at significantly higher computational cost. The work offers a generalizable framework for enhancing ground-based multiband imaging using overlapping space-based data, with practical applicability to upcoming surveys and possible extension to other band combinations.

Abstract

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work we introduce a novel multiband deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the Rubin $r$, $i$, and $z$ bands lie within the Euclid VIS band. The algorithm jointly de-convolves all the data to convert the $r$-, $i$-, and $z$-band Rubin images to the resolution of Euclid by leveraging the correlations between the different bands. We also investigate the performance of deep-learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific Euclid-Rubin combination, offering a versatile solution to improving the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.

Figures (12)

• Figure 1: Filter curves for Euclid and Rubin. The relative filter transmission is shown as a function of the wavelength. The Euclid VIS band overlaps with three of the Rubin filters: $r$, $i$, and $z$.
• Figure 2: Histogram of the HST $F775W$-band magnitude for all galaxies in our dataset after filtering. Note that the HST $F775W$ band matches with the Rubin $i$ band.
• Figure 3: Loss function for the galaxy shown in Fig. \ref{['subfig:mcdec1']}. Convergence is guaranteed at around 100 iterations when the relative change in loss value is $<10^{-3}$ and the curve is flat.
• Figure 4: Unit test to verify that there is no leakage of flux from one channel to another. The recovered Gaussians remain at their original centers.
• Figure 5: DRUNet architecture, which incorporates an additional noise level map as input and integrates U-Net ronnenberger2015unet with ResNet resnet. "SConv" stands for strided convolution, and "TConv" stands for transposed convolution. Image credits: drunet.