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Bradley R Johnson

Publications and source records attributed to Bradley R Johnson.

The Simons Observatory Microwave SQUID Multiplexing Detector Module Design

Advances in cosmic microwave background(CMB)science depend on increasing the number of sensitive detectorsobserving the sky. New instruments deploy large arrays of superconducting transition-edge sensor(TES)bolometers tileddensely into ever larger focal planes. High multiplexing factors reduce the thermal loading on the cryogenic receivers andsimplify their design. We present the design of focal-plane modules with an order of magnitude higher multiplexingfactor than has previously been achieved with TES bolometers. We focus on the novel cold readout component, whichemploys microwave SQUID multiplexing(μmux). Simons Observatory will use 49 modules containing 70,000bolometers to make exquisitely sensitive measurements of the CMB. We validate the focal-plane module design,presenting measurements of the readout component with and without a prototype detector array of 1728 polarization-sensitive bolometers coupled to feedhorns. The readout component achieves a 95% yield and a 910 multiplexing factor.The median white noise of each readout channel is 65pA Hz. This impacts the projected SO mapping speed by<8%,which is less than is assumed in the sensitivity projections. The results validate the full functionality of the module. Wediscuss the measured performance in the context of SO science requirements, which are exceeded.

Heather McCarrick↗

Simons Observatory HoloSim-ML: Machine Learning Applied to the Efficient Analysis of Radio Holography Measurements of Complex Optical Systems

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

Grace E Chesmore↗