Automated Spin Readout Signal Analysis Using U-Net with Variable-Length Traces and Experimental Noise
The work proposes a U-Net architecture to analyze single-shot spin readout traces by treating transition event detection as a point-wise segmentation task in 1D time series data. This method overcomes limitations of fixed-length input methods by handling variable trace lengths directly, providing robust, automated analysis that maintains high accuracy even under experimental noise.