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  • Iterations for active sampling in inelastic neutron scattering

Iterations for active sampling in inelastic neutron scattering

ORCID
0000-0001-9744-6233
Affiliation
Peter Grünberg Institute, Forschungszentrum Jülich & JARA. Faculty of Physics, University of Duisburg-Essen and CENIDE.
AbuAwwad, Nihad;
ORCID
0000-0001-6165-0646
Affiliation
Institute of Materials Science, Technical University Darmstadt
Zhang, Yixuan;
GND
133026914
ORCID
0000-0003-2573-2841
LSF
61216
Affiliation
Peter Grünberg Institute, Forschungszentrum Jülich & JARA. Faculty of Physics, University of Duisburg-Essen and CENIDE
Lounis, Samir;
ORCID
0000-0002-1035-8861
Affiliation
Institute of Materials Science, Technical University Darmstadt
Zhang, Hongbin

We combine Linear Spin Wave Theory with active-learning sampling, resulting in a Kalman Filter enhanced Adversarial Bayesian Optimization (KFABO) algorithm. This algorithm approximates the magnon spectrum using a minimal number of sampling points and iterations. Despite the limited iterations, the algorithm effectively addresses noisy neutron scattering data, providing reliable magnetic interactions that replicate the experimental spectra for 2D CrSBr. It can also reveal hidden or weak interactions, such as those induced by spin-orbit coupling.

The attached files illustrate the sampling process during different iteration scenarios. "Theoretical_SPINW_woDMI.gif" shows the iterations for the case including only Heisenberg exchange (J) interactions. "Theoretical_SPINW_wDMI.gif" displays iterations for the case including both Dzyaloshinskii-Moriya interactions (DMI) and Heisenberg exchange interactions. Finally, "Experimental_SPINW_wDMI.gif" represents the iterations during the fitting of the experimental spin wave data.

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Date Issued:
10.07.2024
DOI:
10.17185/duepublico/82148
Language:
English
Type of Resource:
moving image
Extent:
10.6 MB
Keywords:
Spin wave; Adversarial Bayesian optimization; Kalman filter
Collection:
Research Data
Destatis subject classification:
3610 Physik
Subject categories of the Deutsche Nationalbibliografie:
530 Physics
Hosting institution:
Faculty of Physics, Theoretical Physics
funding:

1. Federal Ministry of Education and Research of Germany in the framework of the Palestinian-German Science Bridge (BMBF grant number 01DH16027).

2. Priority Programmes SPP 2244 "2D Materials Physics of van der Waals heterobilayer"  (project LO 1659/7-1)

3. SPP 2137 “Skyrmionics” (Projects LO 1659/8-1) of the Deutsche Forschungsgemeinschaft (DFG)

4.  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 405553726 - TRR 270.

5. Federal Ministry of Education and Research, and the state governments participating on the basis of the resolutions of the GWK for national high performance computing at universities (www.nhr-verein.de/unsere-partner).

creation/production credits:
We combine Linear Spin Wave Theory (LSWT), Active Learning Sampling, and Adaptive Noise Reduction to form the Kalman Filter enhanced Adversarial Bayesian Optimization Algorithm (KFABO). This algorithm integrates two coupled Bayesian Optimization (BO) algorithms with a Kalman filter. The first BO algorithm, termed fBO, employs trust region Bayesian optimization (Turbo) on our linear response model to search for optimal parameters, aiming to minimize the difference between theoretically predicted and real LSW function values of the measured sample points. The second algorithm, termed sBO, is a standard BO that selects sampling points with maximum information gain relative to the current state, specifically, those points that can better characterize the real LSW function given the current samples and the fitted LSW function.

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