  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.
