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Abstract:

X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes

Abstract:

Autoresonant phase locking of the plasma wakefield to the beat frequency of two driving lasers offers advantages over conventional wakefield acceleration methods, since it requires less demanding laser parameters and is robust to variations in the target plasma density. Here, we investigate the kinetic and nonlinear processes that come into play during autoresonant plasma beat-wave acceleration of electrons, their impact on the field amplitude of the accelerating structure, and on acceleration efficiency. Particle-in-cell simulations show that the process depends on the plasma density in a nontrivial way but can be reliably modeled under specific conditions. Beside recovering previous fluid results in the deeply underdense plasma limit, we demonstrate that robust field excitation can be achieved within a fully kinetic self-consistent modeling. By adjusting the laser properties, we can amplify the electric field to the desired level, up to wave breaking, and efficiently accelerate particles; we provide suggestions for optimized laser and plasma parameters. This versatile and efficient acceleration scheme, producing electrons from tens to hundreds of MeV energies, holds promise for a wide range of applications in research industry and medicine.