Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection

TL;DR

This paper introduces a Bayesian optimization-based method for real-time automatic termination of inelastic neutron-scattering experiments, optimizing bin widths to improve efficiency and reduce measurement time.

Contribution

It presents a novel Bayesian optimization approach for real-time experiment termination and bin-width selection in neutron scattering, enhancing efficiency over previous methods.

Findings

Bayesian optimization reduces search cost to about 10% of exhaustive search.

Optimal bin widths decrease with more data, aligning with instrument resolution limits.

The method effectively prevents excessive measurement beyond equipment resolution.

Abstract

Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multidimensional histograms has been developed and recently validated on real inelastic neutron-scattering data. However, measuring beyond the equipment resolution leads to inefficient use of valuable beam time. To improve experimental efficiency, an automatic termination strategy is essential. We propose a Bayesian-optimization-based method to compute stopping criteria and determine whether to continue or terminate the experiment in real time. In the proposed method, the bin-width optimization is performed using Bayesian optimization to efficiently compute the optimal bin widths. The experiment is terminated when the optimal bin widths become smaller than the target resolutions. In numerical experiments using real inelastic neutron-scattering data, the optimal bin widths decrease as the number of events increases. Even the optimal bin widths for data downsampled to 1/5 are comparable with the resolutions limited by the sample size, choppers, and so on. This implies excessive measurement of the inelastic neutron experiments for the moment. Moreover, we found that Bayesian optimization can reduce the search cost to approximately 10% of an exhaustive search in our numerical experiments.