Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model

TL;DR

This paper introduces a sequential experimental design method for spectral measurements using active learning with parametric models, significantly reducing measurement time while improving model accuracy.

Contribution

It develops a Bayesian active learning approach with parametric models for spectral data, enabling efficient sequential experimental design with complex models.

Findings

Improved accuracy of model selection and parameter estimation.

Reduced measurement time compared to traditional methods.

Effective application to Bayesian spectral deconvolution and Hamiltonian selection.

Abstract

In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequential experimental designs are proposed, in which the subsequent measurement is designed by active learning using the data obtained before the measurement. Conventionally, parametric models are employed in data analysis; when employed for active learning, they are expected to afford a sequential experimental design that improves the accuracy of data analysis. However, due to the complexity of the formulas, a sequential experimental design using general parametric models has not been realized. Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models. In this study, we evaluated the effectiveness of the proposed method by applying it to Bayesian spectral deconvolution and Bayesian Hamiltonian selection in X-ray photoelectron spectroscopy. Using numerical experiments with artificial data, we demonstrated that the proposed method improves the accuracy of model selection and parameter estimation while reducing the measurement time compared with the results achieved without active learning or with active learning using the Gaussian process regression.