Generation and measurement of isolated attosecond pulses with enhanced flux using a two colour synthesized laser field.

We have generated isolated attosecond pulses and performed attosecond streaking measurements using a two-colour synthesized laser field consisting of a strong near-infrared few-cycle pulse and a weaker multi-cycle pulse centred at 400 nm. An actively stabilized interferometer was used to coherently combine the two pulses. Using attosecond streaking we characterised the electric fields of the two pulses and accurately retrieved the spectrum of the multi-cycle pulse. We demonstrated a two-fold increase in the flux of isolated attosecond pulses produced and show that their duration was minimally affected by the presence of the weaker field due to spectral filtering by a multilayer mirror.

Chirp-control of resonant high-order harmonic generation in indium ablation plumes driven by intense few-cycle laser pulses.

We have studied high-order harmonic generation (HHG) in an indium ablation plume driven by intense few-cycle laser pulses centered at 775 nm as a function of the frequency chirp of the laser pulse. We found experimentally that resonant emission lines between 19.7 eV and 22.3 eV (close to the 13th and 15th harmonic of the laser) exhibit a strong, asymmetric chirp dependence, with pronounced intensity modulations. The chirp dependence is reproduced by our numerical time-dependent Schrödinger equation simulations of a resonant HHG by the model indium ion. As demonstrated with our separate simulations of HHG within the strong field approximation, the resonance can be understood in terms of the chirp-dependent HHG photon energy coinciding with the energy of an autoionizing state to ground state transition with high oscillator strength. This supports the validity of the general theory of resonant four-step HHG in the few-cycle limit.

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning.

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.