GalaxyGenius generates synthetic galaxy images for astronomical surveys using outputs from hydrodynamical simulations. The code extracts particle properties from simulation snapshots, assigns spectral energy distributions, and performs radiative transfer calculations to produce ideal data cubes. It then simulates observational images by applying telescope filter throughputs, point spread functions, and instrumental noise. The workflow is organized into modules for data preprocessing, ideal data cube generation, and mock observation. GalaxyGenius supports simulations such as IllustrisTNG and EAGLE and can generate images for multiple ground- and space-based surveys.

Forklens measures weak gravitational lensing signal using a deep-learning methoe. It measures galaxy shapes (shear) and corrects the smearing of the point spread function (PSF, an effect from either/both the atmosphere and optical instrument). It contains a custom CNN architecture with two input branches, fed with the observed galaxy image and PSF image, and predicts several features of the galaxy, including shape, magnitude, and size. Simulation in the code is built directly upon GalSim (ascl:1402.009).