Figure 1: Scientists, facility engineers, and administrators of high-power-lasers are faced with many questions related to building new digital infrastructure for facility control and scientific data management.

Digital data and control continue to revolutionize the day-to-day work of scientists and engineers in every domain. The high-power-laser science and engineering community, while no exception to the overall trend, has not been on the leading edge of big-data and automation in science. Modest laser-repetition-rates have sustained a methodology of detailed human engagement in the planning, execution, and analysis of each individual experimental laser shot.

This very individualized and manual approach to experimental equipment and data has served the community well, supporting the characterization of key physical processes underpinning light-matter interactions using repetition-rate limited experimental technology. However, as technology evolves, that same approach may not continue to produce the best science. Ongoing evolution in laser technology (with concurrent evolution in experimental targetry and diagnostics) is reducing the time between laser shots from hours, to minutes, to milliseconds – at small and large facilities alike. With contemporary high-power lasers, it is becoming increasingly impractical for staff and scientists to enter the laboratory space before and after each laser shot to manually control and manipulate experimental components and data. Furthermore, it is becoming unwieldy to manually manage (e.g. collate, analyze, visualize, and store) each individual data element of each single-shot dataset.

Upgrades to data and control infrastructure, aiming to address these limitations, are already occurring at high-power-laser facilities throughout the world. A distributed networked control system can facilitate laboratory-wide operational speeds and closed-loop approaches which humans cannot achieve. A consistent approach to managing data can increase data accessibility to scientists and external partners, increase reliability of metadata, and increase re-usability of data-analysis software.

There are many worthwhile approaches, and equally many pitfalls, when re-working the data and control architectures for next-generation high-power-laser science. Surprisingly, many research teams are making major architectural changes in relative isolation. The authors of a new article in High Power Laser Science and Engineering believe this moment presents an opportunity to share community knowledge and help members of the community bootstrap digital infrastructure with one another.

They pulled together a diverse set of stakeholders from the high-power laser community; authors are affiliated with eight institutions in the USA and Europe. Substantial writing was performed by each author to present a high-level insight into the state-of-the-art in their sub-area of experience and expertise. Together, they created a manuscript containing both their individual and collective perspectives on the next generation of control and data pipelines for high-power laser science. The target audience for this manuscript is scientists, facility engineers, and administrators at high-power laser facilities.

The authors compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and they illustrate these topics with case studies from the community. They explore considerations for practical facility-level decision-making in these areas, and they highlight several specific control systems and approaches to data management. They assert that by taking steps now to communicate and synchronize, the high-power-laser community can access the benefits (and mitigate the challenges) of next-generation-facility digital infrastructure.

This manuscript is published open-access as part of the Special Issue: Future Control Systems and Machine Learning at High Power Laser Facilities 2022 in Volume 11, Issue 5 of High Power Laser Science and Engineering. The manuscript can be accessed and read through the following link and citation: Scott Feister, Kevin Cassou, Stephen Dann, Andreas Döpp, Philippe Gauron, Anthony J. Gonsalves, Archis Joglekar, Victoria Marshall, Olivier Neveu, Hans-Peter Schlenvoigt, Matthew J. V. Streeter, Charlotte A. J. Palmer. Control systems and data management for high-power laser facilities[J]. High Power Laser Science and Engineering, 2023, 11(5): 05000e56.