Speaker
Description
The emergence of high-power laser systems approaching kilohertz repetition rates presents both a challenge and an opportunity for the next generation of laser-plasma accelerators (LPAs). The vast amount of data generated at these high repetition rates opens the door to novel, data-driven approaches for improving stability, beam quality, and reliability—critical steps toward making LPAs viable for real-world applications.
In this talk, we present recent developments at DESY’s Kaldera project, focusing on real-time data acquisition and processing strategies tailored for high-rep-rate operation. We explore how this data forms the foundation for advanced optimization techniques, enabling closed-loop feedback control and providing deeper insights into the complex dynamics of laser-plasma acceleration. By leveraging machine learning, real-time diagnostics, and intelligent control systems, we outline a path toward more robust and efficient laser-plasma acceleration.
