Advanced model for microwave SQUID multiplexers accounts for readout power effects and inhomogeneous Josephson junctions, yielding significantly improved agreement with experimental data.
Fleischmann , author C
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Machine learning methods are explored for pulse classification, artifact rejection, and shape analysis in metallic magnetic calorimeters to improve scalability over traditional signal processing.
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Advanced microwave SQUID multiplexer model incorporating readout power effects and Josephson junction inhomogeneities
Advanced model for microwave SQUID multiplexers accounts for readout power effects and inhomogeneous Josephson junctions, yielding significantly improved agreement with experimental data.
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Machine Learning Approaches for Improved Scalability of Metallic Magnetic Calorimeters
Machine learning methods are explored for pulse classification, artifact rejection, and shape analysis in metallic magnetic calorimeters to improve scalability over traditional signal processing.