Sequential Bayesian experimental design for everyday measurements

This talk gives an overview of software that increases speed and precision of routine measurements. In experiments where one would traditionally fit data to extract parameters, the ‘optbayesexpt’ package recommends measurement settings “on the fly” based on analysis of accumulating data. The algorithm uses optimal Bayesian experimental design to predict settings with the best chance of reducing parameter uncertainty. In simulations and in tests, we demonstrate order-of-magnitude speedup in measurements of Lorentzian peaks and significant speedup of exponential decay measurements relative to measure-then-fit strategies. The package, written in Python, includes a server script that communicates with instrument control software in any popular instrument control language. Demonstrations include magnetic resonance spectra of NV centers in diamond, and simulations include calibration of π-pulses for spin control. See the manual at https://pages.nist.gov/optbayesexpt/ and software at https://github.com/usnistgov/optbayesexpt.