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. (C) 2021 Optical Society of America
Chesmore, G.e., Adler, A.e., Cothard, N.f., Dachlythra, N., Gallardo, P.a., Gudmundsson, J., et al. (2021). Simons Observatory HoloSim-ML: machine learning applied to the efficient analysis of radio holography measurements of complex optical systems. APPLIED OPTICS, 60(29), 9029-9035 [10.1364/AO.435007].
Simons Observatory HoloSim-ML: machine learning applied to the efficient analysis of radio holography measurements of complex optical systems
Puglisi G.;
2021-01-01
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 micrometer accuracy. We apply this approach to the example of the Simons Observatory 6 m telescope. (C) 2021 Optical Society of AmericaI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
