RESUMO
This contribution presents the initial characterization of the pump-probe performance at the Small Quantum Systems (SQS) instrument of the European X-ray Free Electron Laser. It is demonstrated that time-resolved experiments can be performed by measuring the X-ray/optical cross-correlation exploiting the laser-assisted Auger decay in neon. Applying time-of-arrival corrections based on simultaneous spectral encoding measurements allow us to significantly improve the temporal resolution of this experiment. These results pave the way for ultrafast pump-probe investigations of gaseous media at the SQS instrument combining intense and tunable soft X-rays with versatile optical laser capabilities.
RESUMO
Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise in improving plant performance and reducing tuning times. At the same time, reinforcement learning (RL) is a capable method of learning intelligent controllers, and recent work shows that RL can also be used to train domain-specialised optimisers in so-called reinforcement learning-trained optimisation (RLO). In parallel efforts, both algorithms have found successful adoption in particle accelerator tuning. Here we present a comparative case study, assessing the performance of both algorithms while providing a nuanced analysis of the merits and the practical challenges involved in deploying them to real-world facilities. Our results will help practitioners choose a suitable learning-based tuning algorithm for their tuning tasks, accelerating the adoption of autonomous tuning algorithms, ultimately improving the availability of particle accelerators and pushing their operational limits.