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Volume 228, 2021

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

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Abstract

We have constructed deep neural networks, which can map fluctuating photo-electron spectra obtained from noisy pulses to spectra from noise-free pulses. The network is trained on spectra from noisy pulses in combination with random Hamilton matrices, representing systems which could exist but do not necessarily exist. In [Giri et al., Phys. Rev. Lett., 2020, 124, 113201] we performed a purification of fluctuating spectra, that is, mapping them to those from Fourier-limited Gaussian pulses. Here, we investigate the performance of such neural-network-based maps for predicting spectra of double pulses, pulses with a chirp and even partially-coherent pulses from fluctuating spectra generated by noisy pulses. Secondly, we demonstrate that along with purification of a fluctuating double-pulse spectrum, one can estimate the time-delay of the underlying double pulse, an attractive feature for single-shot spectra from SASE FELs. We demonstrate our approach with resonant two-photon ionization, a non-linear process, sensitive to details of the laser pulse.

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

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Article information


Submitted
11 Oct 2020
Accepted
15 Dec 2020
First published
15 Dec 2020

This article is Open Access

Faraday Discuss., 2021,228, 502-518
Article type
Paper

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

S. K. Giri, L. Alonso, U. Saalmann and J. M. Rost, Faraday Discuss., 2021, 228, 502 DOI: 10.1039/D0FD00117A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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