Information-Optimal Sensing and Control in High-Intensity Laser Experiments

TL;DR

This paper presents a unified information-theoretic framework for high-intensity laser experiments, enabling optimal measurement, adaptive sensing, and Bayesian optimization to improve precision and control in complex, low-repetition-rate systems.

Contribution

It introduces a comprehensive Bayesian approach that captures full spatio-temporal laser pulse information and extends to active sensing and optimization strategies, representing a paradigm shift in experimental measurement.

Findings

Demonstrated complete spatio-temporal characterization of laser pulses.

Showed how Bayesian methods improve measurement precision using temporal correlations.

Extended principles to active sensing and Bayesian optimization in laser experiments.

Abstract

High-intensity laser systems present unique measurement and optimization challenges due to their high complexity, low repetition rates, and shot-to-shot variations. We discuss recent developments towards a unified framework based on information theory and Bayesian inference that addresses these challenges. Starting from fundamental constraints on the physical field structure, we recently demonstrated how to capture complete spatio-temporal information about individual petawatt laser pulses. Building on this foundation, we demonstrate how Bayesian frameworks can leverage temporal correlations between consecutive pulses to improve measurement precision. We then extend these concepts to active sensing strategies that adaptively select measurements to maximize information gain, exemplified through Bayesian autocorrelation spectroscopy. Finally, we show how these information-optimal measurement principles naturally extend to Bayesian optimization. This progression represents a paradigm shift where measurement devices transition from passive data collectors to active participants in complex experiments.