Abstract

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.

Highlights

  • Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter

  • Owing to thermal load, data readout and storage limits, many of these essential diagnostics, such as X-band transverse deflecting cavity (XTCAV), will not be compatible with the high-repetition rate of the generation of X-ray free-electron lasers (XFELs) driven by superconducting linear accelerators (LINAC) operating at megahertz rates such as the European XFEL31 or the LINAC Coherent Light Source (LCLS)-II32

  • Using data from LCLS, we found that much of the information usually extracted from slow, complex diagnostics such as the pump-probe delay in the twin bunch mode, the photon energy or even the spectral shape of the X-ray pulses, is strongly correlated to electron bunch and X-ray properties measured by fast diagnostics

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Summary

Introduction

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. Using data from LCLS, we found that much of the information usually extracted from slow, complex diagnostics such as the pump-probe delay in the twin bunch mode, the photon energy or even the spectral shape of the X-ray pulses, is strongly correlated to electron bunch and X-ray properties measured by fast diagnostics While these correlations are driven by physical processes, performing accurate direct modelling of every experimental aspect in machines as complex as XFELs is currently not possible. We use generic linear, quadratic and more complex, but well-known, machine learning models[40], such as artificial neural networks (ANN)[41] or support vector regression (SVR)[42] to describe the non-trivial hidden correlations and make predictions of the fluctuations in the variables measured by the complex diagnostics using the fluctuations measured with the simple diagnostics as input Using this technique at the LCLS, we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two X-ray pulses. This approach could potentially be used at the generation of high-repetition rate XFELs to provide accurate knowledge of complex X-ray pulses at the full repetition rate, as well as lessening the load on the data stream requirements in existing machines

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