CNN dataset for Overcoming Residual Timing Jitter in Pump-Probe interferometry via Weak Value Amplification and Deep Learning

CNN dataset for a hybrid methodology that synergistically combines weak value amplification (WVA) and deep learning to suppress the limiting effects of residual timing jitter in pump-probe interferometry, achieved through simulations of pump-induced time delays at a few-attosecond resolution. A convolutional neural network regressor (CNN-Regressor) for direct delay estimation and a classifier (CNN-Classifier) for discrete delay categorization are employed to perform high-precision parameter estimators. Both networks were trained under identical conditions: an initial learning rate of 0.001, a maximum of 1000 iterations, and Training:Validation ratios of 8000:2000. For the CNN-regressor, each grayscale interferogram is assigned a continuous $\tau$ value as its label. In contrast, the CNN-classifier categorizes $\tau$ into 100 discrete classes (0 as, 0.1 as 0.2 as... 9.9 as), where each class represents a bin centered at a specific $\tau$ value. To emulate realistic experimental variability, the actual $\tau$ values within each class are randomized uniformly around the bin center with a tolerance of 10 as RMS, representing the time jitter.