Self-Supervised Deep Learning for Attosecond and Femtosecond Pulse Reconstruction

The retrieval of attosecond and femtosecond pulses from attosecond streaking spectrograms is a high-dimensional ill-posed inverse problem. The traditional FROG CRAB (frequency-resolved optical gating for complete reconstruction of attosecond bursts) method relies on the central momentum approximation, while supervised deep learning is constrained by the scarcity of labeled samples and lacks interpretability. In this paper, we propose a self-supervised collaborative reconstruction framework incorporating physics-based constraints, which integrates light-field representation, a differentiable physical model, and a prior regularization module. This framework requires only a small number of unlabeled samples for training and performs direct inference, thereby achieving collaborative high-precision retrieval of attosecond extreme ultraviolet (XUV) and femtosecond infrared pulses. Reconstructions from simulated and measured streaking spectrograms demonstrate that, compared to FROG CRAB, our self-supervised approach is more accurate even for extremely low signal-to-noise ratios, offering a novel, intelligent reconstruction solution for attosecond metrology with limited data and low photon flux.