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arXiv · 2107.04138

The Simons Observatory: HoloSim-ML: machine learning applied to the efficient analysis of radio holography measurements of complex optical systems

Grace E. Chesmore·Alexandre E. Adler·Nicholas F. Cothard·Nadia Dachlythra·Patricio A. Gallardo·Jon Gudmundsson·Bradley R. Johnson·Michele Limon·Jeff McMahon·Federico Nati·Michael D. Niemack·Giuseppe Puglisi·Sara M. Simon·Edward J. Wollack·Kevin Wolz·Zhilei Xu·Ningfeng Zhu
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Abstract

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 micron accuracy. We apply this approach to the example of the Simons Observatory 6m telescope.

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Grace E. Chesmore, Alexandre E. Adler, Nicholas F. Cothard, Nadia Dachlythra, Patricio A. Gallardo, Jon Gudmundsson, Bradley R. Johnson, Michele Limon, Jeff McMahon, Federico Nati, Michael D. Niemack, Giuseppe Puglisi, Sara M. Simon, Edward J. Wollack, Kevin Wolz, Zhilei Xu, Ningfeng Zhu. 2021-07-08. The Simons Observatory: HoloSim-ML: machine learning applied to the efficient analysis of radio holography measurements of complex optical systems. https://doi.org/10.1364/ao.435007

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