LPA Online Workshop on Control Systems and Machine Learning

Sitzung

Experiments with in-situ analysis and active feedback control

25.01.2022, 16:00

Vorsitzende der Sitzung

Experiments with in-situ analysis and active feedback control

• Scott Feister

14. Data-driven insights and operation at LUX

Manuel Kirchen (DESY)

25.01.22, 16:15

Laser-plasma acceleration (LPA) promises compact sources of high-brightness electron beams for science and industry. However, transforming LPA into a technology to drive real-world applications remains a challenge. In this talk, we discuss how the design and operational principles of the LUX experiment allow us to adopt data-driven approaches to understanding and improving the performance of...

33. Enhanced Diagnostic Analysis through Deep Learning

Derek Mariscal (Lawrence Livermore National Laboratory)

25.01.22, 16:45

Deep learning can be used to replace time-consuming diagnostic analysis. With further development, experimental data from high-rep-rate capable laser facilities can be analyzed accurately and at rates fast enough to enable self-driving experiments. The general process for developing neural networks for analyzing data from a proton beam diagnostic will be discussed.

36. Control and Automation of Plasma Accelerators

Rob Shalloo (DESY)

25.01.22, 17:20

35. Utilizing high-repetition-rate capabilities with adaptive optical systems

Dr. Jinpu Lin (University of Michigan / currently at: LMU Munich)

25.01.22, 17:40

Abstract: While the latest technologies have enabled unprecedented laser intensities, the highest energy laser facilities usually operate in single-shot mode, namely firing a few shots a day. On the other side, the development of laser systems with higher repetition rates but lower peak power is always of fundamental interest, particularly for applications. Applications of the particle beams...

34. Gaussian Process Regression in Laser Plasma Experiments

Matthew Streeter (Queen's University Belfast)

25.01.22, 18:00