From DESI to Euclid: A Generative Bridge to Improve Measurements of Galaxy Structure

Abstract

Ground-based seeing imprints size-dependent biases on galaxy structural parameters, yet the high-resolution space-based imaging needed to improve these measurements currently covers only a small fraction of the sky. We close this gap with a generative model that produces Euclid-like VIS predictions from DESI imaging of Bright Galaxy Survey (BGS) targets. Our predictions remain Fourier-correlated with Euclid VIS images down to 0.37'', compared with 1.41'' and 1.00'' for the DESI $r$- and $z$-band inputs, corresponding to improvements by factors of $\sim 3.8$ and $\sim 2.7$, respectively. Although this correlation does not extend down to 0.16'', the characteristic Euclid VIS PSF FWHM, structural measurements from these predictions already show reduced biases relative to the DESI $r$-band structure measurements: the Petrosian radius bias falls to +0.072'' (from -0.845''), independent of galaxy size; the bias in the Sérsic effective radius ($R_{\mathrm{e}}$) drops to -0.018'' (from -0.322''); and the Sérsic-index bias to +0.093 (from +0.262). We release these predictions over the Euclid DR1 footprint as the Euclid-like Predictions of BGS (\textbf{E-BGS}), which can be blindly validated once DR1 is public.

10 pages, 6 figures, accepted by ApJL. Code and prediction available; feel free to download, use, and build on them. Prediction: https://doi.org/10.5281/zenodo.21032414