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Journal Article

Purifying Electron Spectra from Noisy Pulses with Machine Learning Using Synthetic Hamilton Matrices

MPS-Authors
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Giri,  Sajal Kumar
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Saalmann,  Ulf
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Rost,  Jan M.
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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1908.02600.pdf
(Preprint), 519KB

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Citation

Giri, S. K., Saalmann, U., & Rost, J. M. (2020). Purifying Electron Spectra from Noisy Pulses with Machine Learning Using Synthetic Hamilton Matrices. Physical Review Letters, 124(11): 113201. doi:10.1103/PhysRevLett.124.113201.


Cite as: http://hdl.handle.net/21.11116/0000-0006-494E-2
Abstract
Photoelectron 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 Schrodinger 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, nonlinear processes which are particularly sensitive to pulse fluctuations. This is possible without training on those systems.