Control systems and data management for high-power laser facilities

Abstract

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community.

Proton and helium ions acceleration in near-critical density gas targets by short-pulse Ti:Sa PW-class laser

“…There is room for improvement if the residual gas is quickly extracted (via a gas catcher or isolation of the interaction region). This result opens the possibility of automatization and control of the particle source at HRR (Feister et al 2023).…”

Machine learning of hidden variables in multiscale fluid simulation

“…Specifically, we use NumPy [40], SciPy [41], matplotlib [42], Xarray [43], JAX [44], and Diffrax [45] while the neural network is managed using Equinox [46]. The numerical experiments and data are managed using MLFlow [47] as discussed in [48].…”