Deep learning in attosecond metrology

Brunner, Christian; Duensing, Andreas; Schröder, Christian; Mittermair, Michael; Golkov, Vladimir; Pollanka, Maximilian; Cremers, Daniel; Kienberger, Reinhard

doi:10.1364/oe.452108

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…Not surprisingly, CNN2, which was trained on data with an intermediate level of noise, performs best overall, providing reconstructions ε field ≲ 0.13 for data with a SNR down to about 10. This is consistent with the finding of the previous studies [30,31].…”

Section: Resilience To Noisesupporting

confidence: 94%

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Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks

Hajivassiliou,

Kassapis,

Tisch

2023

New J. Phys.

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Attosecond streaking is a powerful and versatile technique that allows the full-field characterisation of femtosecond to attosecond optical pulses. It has been instrumental in the verification of attosecond pulse generation and probing of ultrafast dynamics in matter. Recently, machine learning (ML) has been applied to retrieve the fields from streaking data (White and Chang 2019 Opt. Express 27 4799; Zhu et al 2020 Sci. Rep. 10 5782; Brunner et al 2022 Opt. Express 30 15669–84). This offers a number of advantages compared with traditional iterative algorithms, including faster processing and better resilience to noise. Here, we implement a ML approach based on convolutional neural networks and limit the search to physically realistic pulses that can be specified with a small number of parameters. This leads to substantial reductions in both training and retrieval times, enabling near kHz retrieval rates. We examine how the retrieval performance is affected by noise, and for the first time in this context, study the effect of missing data. We show that satisfactory retrievals are still possible with signal to noise ratios as low as 10, and with up to 40 % of data missing.

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Section: Resilience To Noisesupporting

confidence: 94%

“…In recently published work [31], the authors describe the implementation of a data pipeline for processing streaking traces using ML, including preprocessing of the traces to improve the performance of their NNs. They used common CNN architectures, including Google's GoogLeNet.…”

Section: Previous Work Using Nnsmentioning

confidence: 99%

Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks

Hajivassiliou,

Kassapis,

Tisch

2023

New J. Phys.

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“…where µ m (X), σ m (X) are the mean and variance predicted by the mth model in the ensemble [66]. This goes beyond studies such as [27], where an ensemble of predictions is used to produce the uncertainty.…”

Section: Predictive Uncertainty Estimationmentioning

confidence: 99%

“…Most studies, however, focus on a proof of principle, using only theoretical data. Notable exceptions are, reference [26], where CNNs were used to extract molecular structure parameters from experimental laser-induced electron diffraction images, and reference [27], where deep neural networks were applied to streaking traces for parameter extraction and prediction of uncertainties. Unfortunately, the analytical power of ML-assisted imaging is limited if the laser pulse parameters cannot be accurately measured.…”

Section: Introductionmentioning

confidence: 99%

Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

et al. 2023

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Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to `invert' experimental data. Pulse characterization is used as a `testing ground', specifically we retrieve the laser intensity, where `traditional' measurements typically lead to 20% uncertainty. We report on crucial data augmentation techniques required to successfully train on theoretical data and return consistent results from experiments, including accounting for detector saturation. The same procedure can be repeated to apply CNNs in a range of scenarios for strong-field ionization. Using a predictive uncertainty estimation, reliable laser intensity uncertainties of a few percent can be extracted, which are consistently lower than those given by traditional techniques. Using interpretability methods can reveal parts of the distribution that are most sensitive to laser intensity, which can be directly associated with holographic interferences. The CNNs employed provide an accurate and convenient ways to extract parameters, and represent a novel interpretational tool for strong-field ionization spectra.

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“…During the last decade, the emergence of artificial intelligence (AI) has provided a new paradigm to perform advanced simulations, and in particular, its application in ultrafast science has provided new routes to predict the properties of x-ray pulses [4], or to speed retrieval algorithms to characterize attosecond pulses [5], among others. In this work, we use neural networks (NN) to obtain complete 3D-TDSE-based macroscopic HHG calculations driven by structured laser beams in low density gas jets, and demonstrate that HHG simulation methods can benefit from AI not only to speed-up the calculations, but to reveal hidden signatures that are neglected in standard approximations [8].…”

Section: Introductionmentioning

confidence: 99%

Machine-learning applied to the simulation of high harmonic generation driven by structured laser beams

Serrano,

Pablos-Marín,

Hernández-García

2023

EPJ Web Conf.

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High harmonic generation (HHG) is one of the richest processes in strong-field physics. It allows to up-convert laser light from the infrared domain into the extreme-ultraviolet or even soft x-rays, that can be synthesized into laser pulses as short as tens of attoseconds. The exact simulation of such highly non-linear and non-perturbative process requires to couple the laser-driven wavepacket dynamics given by the three-dimensional time-dependent Schrödinger equation (3D-TDSE) with the Maxwell equations to account for macroscopic propagation. Such calculations are extremely demanding, well beyond the state-of-the-art computational capabilities, and approximations, such as the strong field approximation, need to be used. In this work we show that the use of machine learning, in particular deep neural networks, allows to simulate macroscopic HHG within the 3D-TDSE, revealing hidden signatures in the attosecond pulse emission that are neglected in the standard approximations. Our HHG method assisted by artificial intelligence is particularly suited to simulate the generation of soft x-ray structured attosecond pulses.

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Deep learning in attosecond metrology

Cited by 10 publications

References 23 publications

Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks

Rapid retrieval of femtosecond and attosecond pulses from streaking traces using convolutional neural networks

Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

Machine-learning applied to the simulation of high harmonic generation driven by structured laser beams

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