Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot

Abstract

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.

Perspectives for analyzing non-linear photo-ionization spectra with deep neural networks trained with synthetic Hamilton matrices

“…Again we normalize the pulse energy, here by means of N β , as before in Eqs. (1) and (9). The predicted spectra are shown in Fig.…”

“…Machine learning (ML) has recently been applied not only in physics 1,2,3 , but more specifically also in strong-field physics 4,5,6 . One of the most abundant topic has been the reconstruction of the temporal shape of an ultrashort laser pulse, aided by ML techniques 7,8,9 . The most popular technique for this reconstruction have been different variants of streaking techniques which require normally considerable additional experimental effort, namely a Terahertz laser light source.…”

“…However, also a Max Planck Institute for the Physics of Complex Systems Nöthnitzer Str. 38, 01187 Dresden, Germany b E-mail: sajal@pks.mpg.de c E-mail: lalonso@pks.mpg.de d E-mail: us@pks.mpg.de e E-mail: rost@pks.mpg.de a direct method from single-shot spectra has been introduced 9 .…”

Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces

“…Previously, neural networks were shown to be successful in pulse retrieval from 2D data-sets: FROG traces [21,22,23,24], D-Scan traces [25], speckles at the output of a multi-mode fiber [26], and streak traces characterising attosecond pulses [27]. However, neural-network-assisted reconstruction of ultrashort pulses from 1D interferometric cross-correlation data-sets, to the best of our knowledge, have not been demonstrated previously, and our proof-of-principle work aims to fill this gap, as summarized below.…”

Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials

“…Recent trends in artificial intelligence have led to a proliferation of studies on deep learning (DL) algorithm and its utilization in diverse fields, such as biology [26][27][28], chemistry [29][30][31], and physics [32][33][34]. In particular, one important aspect pertaining to DL is its capability of characterizing and predicting the physical properties for photonic structures [35][36][37], including the reconstruction of ultrashort pulses [38,39], the wave-front sensing [40], and the design of metasurfaces [41], chiral metamaterials [42,43] and electromagnetic nanostructures [44][45][46][47]. Furthermore, the DL scheme has also penetrated into computational physics, covering the areas of estimating stress distribution [48], assisting computational mechanics [49], capturing nonlinear material behaviors [50] and predicting plasmonic colors [51], whose main advantages over the conventional finite element method are that it not only speeds up the investigation process, but also creates many nonintuitive designs with distinguished performance.…”