Astronomical Image Processing Benchmark Study for Various Telescope Aperture Shapes

We explore the impact of different telescope apertures on the image simulation and deconvolution processes within the context of a synthetic star field. Using HCIPy and Python programming, we modelled six telescope apertures namely Circular, Hexagonal, Elliptical (with horizontal and vertical major axes), segmented hexagonal (JWST), and obstructed circular (HST). We calculated Point Spread Functions (PSFs) for each aperture, incorporating surface shape-induced wavefront aberrations, convolved them with a synthetic star field spanning a range of brightness magnitudes, and introduced photon and detector noise layers to simulate realistic imaging conditions. Subsequent deconvolution using the Richardson-Lucy algorithm allowed for an analysis of deconvolution accuracy based on parameters like average distance between stars and differences in the number of stars between original and deconvolved images. Results indicate that the choice of telescope aperture significantly influences both simulated images and deconvolution outcomes, with brightness magnitude also playing a crucial role. The study highlights the necessity of optimizing image processing pipelines and Deconvolution algorithms tailored to each aperture shapes and their corresponding PSFs, emphasizing the pivotal role of aperture selection and optimization in achieving accurate astronomical imaging performance.