Diffusion-based Galaxy Simulations for the Roman High Latitude Survey
Diana Scognamiglio, Jake H. Lee, Eric Huff, Sergi R. Hildebrandt, and Shoubaneh Hemmati

TL;DR
This paper introduces a diffusion-based method to generate realistic galaxy images for the Roman Space Telescope, improving weak lensing simulations by capturing complex galaxy morphologies from high-resolution observations.
Contribution
The authors develop and validate a diffusion probabilistic model trained on JWST data to produce high-fidelity, multi-band galaxy images tailored for Roman weak lensing analysis.
Findings
Generated galaxy samples match observed distributions of key properties.
The diffusion model reproduces covariance structures of galaxy features.
Results show the method as a scalable alternative to analytic simulations.
Abstract
Future weak lensing analyses with the Nancy Grace Roman Space Telescope will require highly realistic image simulations to control shear systematics at unprecedented precision. A key limitation of existing approaches is their reliance on analytic light-profile models, which cannot fully capture the complex, non-parametric morphologies revealed by high-resolution observations. We present a diffusion-based framework for generating realistic galaxy image simulations tailored to the weak lensing requirements of the Roman High Latitude Survey. We construct Roman-like galaxy images from multi-band JWST/NIRCam observations in the GOODS-S and GOODS-N fields, transforming them into the Roman observing regime through point-spread-function matching, pixel-scale conversion, and interloper masking that preserves correlated noise properties. These data are used to train a denoising diffusion…
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