Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning.

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

SUBMITTER: Alaa El-Din KK 

PROVIDER: S-EPMC10973530 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature