Noise-dependent bias in quantitative STEM-EMCD experiments revealed by bootstrapping

TL;DR

This paper demonstrates that noise in EMCD measurements biases magnetic moment estimates, using bootstrapping and Monte Carlo simulations to analyze error distributions and propose bias mitigation guidelines.

Contribution

It introduces a bootstrapping approach to quantify noise-dependent bias in EMCD experiments, revealing how noise affects magnetic moment estimations.

Findings

Noisy EMCD signals bias magnetic moment estimates

Bootstrapping reveals noise-dependent error distributions

Guidelines proposed to recognize and minimize bias

Abstract

Electron magnetic circular dichroism (EMCD) is a powerful technique for estimating element-specific magnetic moments of materials on nanoscale with the potential to reach atomic resolution in transmission electron microscopes. However, the fundamentally weak EMCD signal strength complicates quantification of magnetic moments, as this requires very high precision, especially in the denominator of the sum rules. Here, we employ a statistical resampling technique known as bootstrapping to an experimental EMCD dataset to produce an empirical estimate of the noise dependent error distribution resulting from application of EMCD sum rules to bcc iron in a 3 beam orientation. We observe clear experimental evidence that noisy EMCD signals preferentially bias the estimation of magnetic moments, further supporting this with error distributions produced by Monte-Carlo simulations. Finally, we propose guidelines for the recognition and minimization of this bias in the estimation of magnetic moments.