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Proceedings Paper

Unconventional imaging with contained granular media
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Paper Abstract

Typically, the cost of a space-borne imaging system is driven by the size and mass of the primary aperture. The solution that we propose uses a method to construct an imaging system in space in which the nonlinear optical properties of a cloud of micron-sized particles, shaped into a specific surface by electromagnetic means, and allows one to form a very large and lightweight aperture of an optical system, hence reducing overall mass and cost. Recent work at JPL has investigated the feasibility of a granular imaging system, concluding that such a system could be built and controlled in orbit. We conducted experiments and simulation of the optical response of a granular lens. In all cases, the optical response, measured by the Modulation Transfer Function, of hexagonal reflectors was closely comparable to that of a conventional spherical mirror. We conducted some further analyses by evaluating the sensitivity to fill factor and grain shape, and found a marked sensitivity to fill factor but no sensitivity to grain shape. We have also found that at fill factors as low as 30%, the reflection from a granular lens is still excellent. Furthermore, we replaced the monolithic primary mirror in an existing integrated model of an optical system (WFIRST Coronagraph) with a granular lens, and found that the granular lens that can be useful for exoplanet detection provides excellent contrast levels. We will present our testbed and simulation results in this paper.

Paper Details

Date Published: 6 September 2017
PDF: 20 pages
Proc. SPIE 10410, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017, 104100W (6 September 2017); doi: 10.1117/12.2272617
Show Author Affiliations
Marco B. Quadrelli, Jet Propulsion Lab. (United States)
Scott Basinger, Jet Propulsion Lab. (United States)
Erkin Sidick, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 10410:
Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017
Jean J. Dolne; Rick P. Millane, Editor(s)

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