http://arxiv.org/abs/2106.09162
In the pursuit of directly imaging exoplanets, the high-contrast imaging community has developed a multitude of tools to simulate the performance of coronagraphs on segmented-aperture telescopes. As the scale of the telescope increases and science cases move toward shorter wavelengths, the required physical optics propagation to optimize high-contrast imaging instruments becomes computationally prohibitive. Gaussian Beamlet Decomposition (GBD) is an alternative method of physical optics propagation that decomposes an arbitrary wavefront into paraxial rays. These rays can be propagated expeditiously using ABCD matrices, and converted into their corresponding Gaussian beamlets to accurately model physical optics phenomena without the need of diffraction integrals. The GBD technique has seen recent development and implementation in commercial software (e.g. FRED, CODE V, ASAP) but appears to lack an open-source platform. We present a new GBD tool developed in Python to model physical optics phenomena, with the goal of alleviating the computational burden for modeling complex apertures, many-element systems, and introducing the capacity to model misalignment errors. This study demonstrates the synergy of the geometrical and physical regimes of optics utilized by the GBD technique, and is motivated by the need for advancing open-source physical optics propagators for segmented-aperture telescope coronagraph design and analysis. This work illustrates GBD with Poisson’s spot calculations and show significant runtime advantage of GBD over Fresnel propagators for many-element systems.
J. Ashcraft and E. Douglas
Fri, 18 Jun 21
49/62
Comments: 13 pages, 9 figures, submitted to SPIE Astronomical Telescopes & Instrumentation 2020