摘要
The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here we introduce the CASTOR image simulator, a Python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated light-cones from the Santa Cruz Semi-Analytic Model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75% complete for point sources down to 27, 29 and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of 2200 deg ² , the Wide survey will detect hundreds of millions of galaxies out to z 4, mostly withM_(∗) ≳ 10⁹ M_(⊙) . The Ultra-Deep survey will probe to z 5, detecting galaxies withM_(∗) ≳ 10⁷ M_(⊙) . These galaxy samples will enable precision measurements of the distribution of star formation in the cosmic web, connecting the growth of stellar mass to the assembly of dark matter halos over two thirds of the history of the Universe, and other core goals of CASTOR's legacy surveys. These image simulations and the tools developed to generate them will be a vital planning tool to estimate CASTOR's performance and iterate the telescope and survey designs prior to launch.