Theoretical investigation of orbital alignment of x-ray-ionized atoms in exotic electronic configurations

We theoretically study orbital alignment in x-ray-ionized atoms and ions, based on improved electronic-structure calculations starting from the Hartree-Fock-Slater model. We employ first-order many-body perturbation theory to improve the Hartree-Fock-Slater calculations and show that the use of first-order-corrected energies yields significantly better transition energies than originally obtained. The improved electronic-structure calculations enable us also to compute individual state-to-state cross sections and transition rates and, thus, to investigate orbital alignment induced by linearly polarized x rays. To explore the orbital alignment of transiently formed ions after photoionization, we discuss alignment parameters and ratios of individual state-resolved photoionization cross sections for initially neutral argon and two exotic electronic configurations that may be formed during x-ray multiphoton ionization dynamics induced by x-ray free-electron lasers. We also present how the orbital alignment is affected by Auger-Meitner decay and demonstrate how it evolves during a sequence of one photoionization and one Auger-Meitner decay. Our present work establishes a step toward investigation of orbital alignment in atomic ionization driven by high-intensity x rays

State-resolved ionization dynamics of a neon atom induced by x-ray free-electron-laser pulses

We present a theoretical framework to describe state-resolved ionization dynamics of neon atoms driven by ultraintense x-ray free-electron-laser pulses. In general, x-ray multiphoton ionization dynamics of atoms have been described by time-dependent populations of the electronic configurations visited during the ionization dynamics, neglecting individual state-to-state transition rates and energies. Combining a state-resolved electronic-structure calculation, based on first-order many-body perturbation theory, with a Monte Carlo rate-equation method enables us to study state-resolved dynamics based on time-dependent state populations. Our results demonstrate that configuration-based and state-resolved calculations provide similar charge-state distributions, but the differences are visible when resonant excitations are involved, which are also reflected in calculated time-integrated electron and photon spectra. In addition, time-resolved spectra of ions, electrons, and photons are analyzed for different pulse durations to explore how frustrated absorption manifests itself during the ionization dynamics of neon atoms

X-ray-induced atomic transitions via machine learning: A computational investigation

Intense x-ray free-electron laser pulses can induce multiple sequences of one-photon ionization and accompanying decay processes in atoms, producing highly charged atomic ions. Considering individual quantum states during these processes provides more precise information about the x-ray multiphoton ionization dynamics than the common configuration-based approach. However, in such a state-resolved approach, extremely huge-sized rate-equation calculations are inevitable. Here we present a strategy that embeds machine-learning models into a framework for atomic state-resolved ionization dynamics calculations. Machine learning is employed for the required atomic transition parameters, whose calculations possess the computationally most expensive steps. We find for argon that both feedforward neural networks and random forest regressors can predict these parameters with acceptable, but limited accuracy. State-resolved ionization dynamics of argon, in terms of charge-state distributions and electron and photon spectra, are also presented. Comparing fully calculated and machine-learning-based results, we demonstrate that the proposed machine-learning strategy works in principle and that the performance, in terms of charge-state distributions and electron and photon spectra, is good. Our work establishes a first step toward accelerating the calculation of atomic state-resolved ionization dynamics induced by high-intensity x rays

X-ray-induced atomic transitions via machine learning: A computational investigation

Intense x-ray free-electron laser pulses can induce multiple sequences of one-photon ionization and accompanying decay processes in atoms, producing highly charged atomic ions. Considering individual quantum states during these processes provides more precise information about the x-ray multiphoton ionization dynamics than the common configuration-based approach. However, in such a state-resolved approach, extremely huge-sized rate-equation calculations are inevitable. Here we present a strategy that embeds machine-learning models into a framework for atomic state-resolved ionization dynamics calculations. Machine learning is employed for the required atomic transition parameters, whose calculations possess the computationally most expensive steps. We find for argon that both feedforward neural networks and random forest regressors can predict these parameters with acceptable, but limited accuracy. State-resolved ionization dynamics of argon, in terms of charge-state distributions and electron and photon spectra, are also presented. Comparing fully calculated and machine-learning-based results, we demonstrate that the proposed machine-learning strategy works in principle and that the performance, in terms of charge-state distributions and electron and photon spectra, is good. Our work establishes a first step toward accelerating the calculation of atomic state-resolved ionization dynamics induced by high-intensity x rays