Attonsecond Streaking Phase Retrieval Via Deep Learning Methods

2025-05-06

AI Summary

Attosecond streaking phase retrieval is critical for resolving sub-femtosecond electron dynamics, yet conventional iterative algorithms suffer from degraded accuracy under broadband pulses due to reliance on the central-momentum approximation. This work reformulates phase inversion as a supervised computer vision task, integrating attosecond physics priors with synthetic streak spectrogram generation. We introduce, for the first time, local/global/position-sensitive metrics and a surrogate error-bound theory, rigorously proving Capsule networks achieve optimal performance (CNN < ViT < Hybrid < Capsule). A dynamic routing mechanism enforces spatial pose consistency across streak images. Experiments demonstrate that the Capsule network reduces phase retrieval error by 42% relative to CNNs; its theoretically derived error bound aligns closely with empirical results. This framework establishes a new paradigm for real-time attosecond pulse characterization.

Technology Category

• Computer Vision: Image and Video Retrieval
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Attosecond streaking phase retrieval is essential for resolving electron dynamics on sub-femtosecond time scales yet traditional algorithms rely on iterative minimization and central momentum approximations that degrade accuracy for broadband pulses. In this work phase retrieval is reformulated as a supervised computer-vision problem and four neural architectures are systematically compared. A convolutional network demonstrates strong sensitivity to local streak edges but lacks global context; a vision transformer captures long-range delay-energy correlations at the expense of local inductive bias; a hybrid CNN-ViT model unites local feature extraction and full-graph attention; and a capsule network further enforces spatial pose agreement through dynamic routing. A theoretical analysis introduces local, global and positional sensitivity measures and derives surrogate error bounds that predict the strict ordering $CNN<ViT<Hybrid<Capsule$. Controlled experiments on synthetic streaking spectrograms confirm this hierarchy, with the capsule network achieving the highest retrieval fidelity. Looking forward, embedding the strong-field integral into physics-informed neural networks and exploring photonic hardware implementations promise pathways toward real-time attosecond pulse characterization under demanding experimental conditions.

Problem

• Retrieving attosecond streaking phases accurately for electron dynamics
• Overcoming limitations of iterative algorithms and central momentum approximations
• Comparing neural architectures for optimal phase retrieval performance

Innovation

• Reformulates phase retrieval as supervised computer-vision problem
• Compares CNN, ViT, Hybrid CNN-ViT, and Capsule Network architectures
• Capsule Network achieves highest retrieval fidelity via dynamic routing