Kalman filter enhanced Adversarial Bayesian optimization for active sampling in inelastic neutron scattering

TL;DR

This paper presents a machine learning algorithm that combines Kalman filter and adversarial Bayesian optimization to efficiently and accurately analyze minimal inelastic neutron scattering data for spin wave characterization in magnetic materials.

Contribution

The paper introduces a novel machine learning approach integrating adaptive noise reduction and active sampling for inelastic neutron scattering data analysis, improving efficiency and accuracy.

Findings

Enhanced analysis of magnon spectra in CrSBr

Significant improvements in data efficiency and accuracy

Applicable to other characterization techniques

Abstract

Spin waves, or magnons, are fundamental excitations in magnetic materials that provide insights into their dynamic properties and interactions. Magnons are the building blocks of magnonics, which offer promising perspectives for data storage, quantum computing, and communication technologies. These excitations are typically measured through inelastic neutron or x-ray scattering techniques, which involve heavy and time-consuming measurements, data processing, and analysis based on various theoretical models. Here, we introduce a machine learning algorithm that integrates adaptive noise reduction and active learning sampling, which enables the restoration from minimal inelastic neutron scattering point data of spin wave information and the accurate extraction of magnetic parameters, including hidden interactions. Our findings, benchmarked against the magnon spectra of CrSBr, significantly enhance the efficiency and accuracy in addressing complex and noisy experimental measurements. This advancement offers a powerful machine learning tool for research in magnonics and spintronics, which can also be extended to other characterization techniques at large facilities.