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In the pursuit of directly imaging exoplanets, the high-contrast imagingcommunity has developed a multitude of tools to simulate the performance ofcoronagraphs on segmented-aperture telescopes. As the scale of the telescopeincreases and science cases move toward shorter wavelengths, the requiredphysical optics propagation to optimize high-contrast imaging instrumentsbecomes computationally prohibitive. Gaussian Beamlet Decomposition (GBD) is analternative method of physical optics propagation that decomposes an arbitrarywavefront into paraxial rays. These rays can be propagated expeditiously usingABCD matrices, and converted into their corresponding Gaussian beamlets toaccurately model physical optics phenomena without the need of diffractionintegrals. The GBD technique has seen recent development and implementation incommercial software (e.g. FRED, CODE V, ASAP) but appears to lack anopen-source platform. We present a new GBD tool developed in Python to modelphysical optics phenomena, with the goal of alleviating the computationalburden for modeling complex apertures, many-element systems, and introducingthe capacity to model misalignment errors. This study demonstrates the synergyof the geometrical and physical regimes of optics utilized by the GBDtechnique, and is motivated by the need for advancing open-source physicaloptics propagators for segmented-aperture telescope coronagraph design andanalysis. This work illustrates GBD with Poisson's spot calculations and showsignificant runtime advantage of GBD over Fresnel propagators for many-elementsystems.

An Open-Source Gaussian Beamlet Decomposition Tool for Modeling Astronomical Telescopes