Gaussian Processes and Bayesian Optimization for High Precision Experiments

Max Lamparth; Mattis Bestehorn; Bastian Märkisch

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arXiv · 2205.07625

Gaussian Processes and Bayesian Optimization for High Precision Experiments

Max Lamparth·Mattis Bestehorn·Bastian Märkisch

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Abstract

High-precision measurements require optimal setups and analysis tools to achieve continuous improvements. Systematic corrections need to be modeled with high accuracy and known uncertainty to reconstruct underlying physical phenomena. To this end, we present Gaussian processes for modeling experiments and usage with Bayesian optimization, on the example of an electron energy detector, achieving optimal performance. We demonstrate the method's strengths and outline stochastic variational Gaussian processes for physics applications with large data sets, enabling new solutions for current problems.

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Max Lamparth, Mattis Bestehorn, Bastian Märkisch. 2022-05-16. Gaussian Processes and Bayesian Optimization for High Precision Experiments. https://arxiv.org/abs/2205.07625

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