Tango Controls and data pipeline for petawatt laser experiments

Self Cite

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.

Section: Comparison Of Platforms For Laboratory Controlmentioning

confidence: 99%

Control systems and data management for high-power laser facilities

Feister,

Cassou,

Dann

et al. 2023

Self Cite

“…Many laboratories are currently stepping up their efforts to integrate prediction and feedback systems into their lasers and experiments [145,146] . One of the first groups to extensively make use of these techniques was the team around A. Maier at DESY's LUX facility [26] .…”

Section: Prediction and Feedbackmentioning

confidence: 99%

Data-driven science and machine learning methods in laser–plasma physics

Döpp

Eberle

Howard

et al. 2023

Self Cite

Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

“…High-power neodymium-glass laser drivers have become the worldwide system of choice for laser fusion research [1,2] . They serve as a crucial platform for research in high-energy-density physics [3] , such as x-ray generation, laser plasma physics [4] , and laboratory astrophysics. Following propagation in a nonlinear medium, a high-power laser undergoes whole-beam self-focusing (WBSF) and SSSF owing to third-order nonlinear polarization; the latter is more destructive [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Theory of small-scale self-focusing of spatially partially coherent beams and its implications for high-power laser systems

Wang,

Zhang,

Zhang

et al. 2024

Based on the paraxial wave equation, this study extends the theory of small-scale self-focusing (SSSF) from coherent beams to spatially partially coherent beams (PCBs) and derives a general theoretical equation that reveals the underlying physics of the reduction in the B-integral of spatially PCBs. From the analysis of the simulations, the formula for the modulational instability (MI) gain coefficient of the SSSF of spatially PCBs is obtained by introducing a decrease factor into the formula of the modulational instability (MI) gain