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MKID digital readout tuning with deep learning.

Dodkins, R. and Mahashabde, S. and O’Brien, K. and Thatte, N. and Fruitwala, N. and Walter, A.B. and Meeker, S.R. and Szypryt, P. and Mazin, B.A. (2018) 'MKID digital readout tuning with deep learning.', Astronomy and computing., 23 . pp. 60-71.


Microwave Kinetic Inductance Detector (MKID) devices offer inherent spectral resolution, simultaneous read out of thousands of pixels, and photon-limited sensitivity at optical wavelengths. Before taking observations the readout power and frequency of each pixel must be individually tuned, and if the equilibrium state of the pixels change, then the readout must be retuned. This process has previously been performed through manual inspection, and typically takes one hour per 500 resonators (20 h for a ten-kilo-pixel array). We present an algorithm based on a deep convolution neural network (CNN) architecture to determine the optimal bias power for each resonator. The bias point classifications from this CNN model, and those from alternative automated methods, are compared to those from human decisions, and the accuracy of each method is assessed. On a test feed-line dataset, the CNN achieves an accuracy of 90% within 1 dB of the designated optimal value, which is equivalent accuracy to a randomly selected human operator, and superior to the highest scoring alternative automated method by 10%. On a full ten-kilopixel array, the CNN performs the characterization in a matter of minutes — paving the way for future mega-pixel MKID arrays.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Publisher statement:© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:02 March 2018
Date deposited:10 April 2018
Date of first online publication:13 March 2018
Date first made open access:13 March 2019

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