The authors propose that optimal measurement and control in high-intensity laser experiments should maximize mutual information about task-relevant parameters, unifying their prior diagnostics and optimization work.
A Bayesian perspective on single-shot laser characterization
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abstract
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.
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Information-Optimal Sensing and Control in High-Intensity Laser Experiments
The authors propose that optimal measurement and control in high-intensity laser experiments should maximize mutual information about task-relevant parameters, unifying their prior diagnostics and optimization work.