Simons Observatory HoloSim-ML: machine learning applied to theefficient analysis of radio holography measurements of complex opticalsystems
2021
Near-field radio holography is a common method for measuring and
aligning mirror surfaces for millimeter and sub-millimeter telescopes. In
instruments with more than a single mirror, degeneracies arise in the
holography measurement, requiring multiple measurements and new fitting
methods. We present HoloSim-ML, a Python code for beam simulation and
analysis of radio holography data from complex optical systems. This code
uses machine learning to efficiently determine the position of hundreds of
mirror adjusters on multiple mirrors with few micrometer accuracy. We
apply this approach to the example of the Simons Observatory 6 m
telescope.
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