Regensburg 2025 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 33: Machine Learning in Dynamics and Statistical Physics I

DY 33.11: Talk

Thursday, March 20, 2025, 12:15–12:30, H47

Kalman filter enhanced adversarial Bayesian optimization for active sampling in inelastic neutron scattering — Yixuan Zhang¹, •Nihad Abuawwad², Samir Lounis², and Hongbin Zhang¹ — ¹TU Darmstadt, Darmstadt, Germany — ²Peter Grünberg Institute (PGI), Jülich, Germany

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.

Keywords: Inelastic neutron scattering; CrSBr; Machine learning; Kalman filter