Correlating Visual Characteristics and Cryogenic Performance of Superconducting Detectors ...
Cryogenic characterization of transition-edge sensor (TES) bolometers is a time- and labor-intensive process. As new experiments deploy larger and larger arrays of TES bolometers, the testing process will become more of a bottleneck. Thus it is desirable to develop a method for evaluating detector p...
| Main Authors: | , , , |
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| Format: | Text |
| Language: | unknown |
| Published: |
arXiv
2022
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.48550/arxiv.2207.14242 https://arxiv.org/abs/2207.14242 |
| Summary: | Cryogenic characterization of transition-edge sensor (TES) bolometers is a time- and labor-intensive process. As new experiments deploy larger and larger arrays of TES bolometers, the testing process will become more of a bottleneck. Thus it is desirable to develop a method for evaluating detector performance at room temperature. One possibility is using machine learning to correlate detectors' visual appearance with their cryogenic properties. Here, we use three engineering-grade TES bolometer wafers from the production cycle for SPT-3G, the current receiver on the South Pole Telescope, to train and test such an algorithm. High-resolution images of these TES bolometers were captured and relevant features were calculated from the images. Cryogenic performance metrics, including a detector's ability to tune and superconducting parameters such as normal resistance, critical temperature, and transition width, were also measured. A random forest algorithm was trained to predict these performance metrics from the ... : 15 pages, 6 figures, Presented at SPIE Astronomical Telescopes + Instrumentation 2022 ... |
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