C. Eberle

h-index8
2papers
187citations

2 Papers

10.4LGNov 30, 2022
Data-driven Science and Machine Learning Methods in Laser-Plasma Physics

Andreas Döpp, Christoph Eberle, Sunny Howard et al.

Laser-plasma physics has developed rapidly over the past few decades as high-power lasers have become both increasingly powerful and more widely available. Early experimental and numerical research in this field was restricted to single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather an increasing amount of data, both in 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 effectively deal with situations in which still only sparse amounts of data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics, including its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

1.2OPTICSFeb 5, 2025
A Bayesian perspective on single-shot laser characterization

J. Esslinger, N. Weisse, C. Eberle et al.

We introduce a Bayesian framework for measuring spatio-temporal couplings (STCs) in ultra-intense lasers that reconceptualizes what constitutes a 'single-shot' measurement. Moving beyond traditional distinctions between single- and multi-shot devices, our approach provides rigorous criteria for determining when measurements can truly resolve individual laser shots rather than statistical averages. This framework shows that single-shot capability is not an intrinsic device property but emerges from the relationship between measurement precision and inherent parameter variability. Implementing this approach with a new measurement device at the ATLAS-3000 petawatt laser, we provide the first quantitative uncertainty bounds on pulse front tilt and curvature. Notably, we observe that our Bayesian method reduces uncertainty by up to 60% compared to traditional approaches. Through this analysis, we reveal how the interplay between measurement precision and intrinsic system variability defines achievable resolution -- insights that have direct implications for applications where precise control of laser-matter interaction is critical.