Purifying electron spectra from noisy pulses with machine learning using synthetic Hamilton matrices

Abstract

Photo-electron spectra obtained with intense pulses generated by free-electron lasers through self-amplified spontaneous emission are intrinsically noisy and vary from shot to shot. We extract the purified spectrum, corresponding to a Fourier-limited pulse, with the help of a deep neural network. It is trained on a huge number of spectra, which was made possible by an extremely efficient propagation of the Schrödinger equation with synthetic Hamilton matrices and random realizations of fluctuating pulses. We show that the trained network is sufficiently generic such that it can purify atomic or molecular spectra, dominated by resonant two- or three-photon ionization, non-linear processes which are particularly sensitive to pulse fluctuations. This is possible without training on those systems.

arXiv comment5 pages, 4 figures (application extended to a molecular system)

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• Perspectives for analyzing non-linear photo ionization spectra with deep neural networks trained with synthetic Hamilton matrices
• Input-Output Optics as a Causal Time Series Mapping: A Generative Machine Learning Solution
• Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints
• A Hybrid Neural Architecture: Online Attosecond X-ray Characterization