An Open-Source Gaussian Beamlet Decomposition Tool for Modeling Astronomical Telescopes. (arXiv:2106.09162v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ashcraft_J/0/1/0/all/0/1">Jaren N. Ashcraft</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Douglas_E/0/1/0/all/0/1">Ewan S. Douglas</a>
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.
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.
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