Large segmented sparse aperture collimation by curvature sensing

Fine alignment of large, segmented telescopes is critical for achieving high angular resolution. Building an instrument with an equally large monolithic aperture is difficult because of the increasing mass and volume. Sparse aperture testing is a lower-cost solution to alignment and metrology, both in the optics shop and at the observatory. We combined sparse aperture testing and curvature sensing to process the highly segmented system's final alignment. First, the stitching error, including tip/tilt/piston and shifting errors, is analyzed theoretically and numerically. These errors are then evaluated by normalized point source sensitivity (PSSn), and the change of PSSn during alignment, which specifies the residual alignment error, is calculated by the defocused donuts. Simulations and experiments demonstrate that the system performance improved by more than 35%. In this paper, we have described the incorporation of sparse aperture testing and curvature sensing algorithms, which can easily cover the tipping and shifting error affecting the traditional methodology.