Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot
This work provides a faster and more robust alternative to iterative methods for real-time probing of ultrafast processes, though it appears incremental as it applies deep learning to an existing diagnostic challenge.
The authors tackled the problem of diagnosing ultrashort laser pulses by proposing an all-in-line single-shot system that uses a deep learning algorithm to recover pulse shape from 2D spatial intensity patterns, demonstrating robustness to noise and inaccuracies in simulations.
We propose a simple all-in-line single-shot scheme for diagnostics of ultrashort laser pulses, consisting of a multi-mode fiber, a nonlinear crystal and a CCD camera. The system records a 2D spatial intensity pattern, from which the pulse shape (amplitude and phase) are recovered, through a fast Deep Learning algorithm. We explore this scheme in simulations and demonstrate the recovery of ultrashort pulses, robustness to noise in measurements and to inaccuracies in the parameters of the system components. Our technique mitigates the need for commonly used iterative optimization reconstruction methods, which are usually slow and hampered by the presence of noise. These features make our concept system advantageous for real time probing of ultrafast processes and noisy conditions. Moreover, this work exemplifies that using deep learning we can unlock new types of systems for pulse recovery.