Volume 228, 2021

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

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

Associated articles

Article information

Article type
Paper
Submitted
11 Oct 2020
Accepted
15 Dec 2020
First published
15 Dec 2020
This article is Open Access
Creative Commons BY license

Faraday Discuss., 2021,228, 502-518

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

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