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Joint machine learning analysis of muon spectroscopy data from different materials

Tula, T., Möller, G., Quintanilla, J., Giblin, S.R., Hillier, A.D., McCabe, E.E., Ramos, S., Barker, D.S. and Gibson, S. 2022. Joint machine learning analysis of muon spectroscopy data from different materials. Journal of Physics: Conference Series 2164 (1) , 012018. 10.1088/1742-6596/2164/1/012018

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Abstract

Machine learning (ML) methods have proved to be a very successful tool in physical sciences, especially when applied to experimental data analysis. Artificial intelligence is particularly good at recognizing patterns in high dimensional data, where it usually outperforms humans. Here we applied a simple ML tool called principal component analysis (PCA) to study data from muon spectroscopy. The measured quantity from this experiment is an asymmetry function, which holds the information about the average intrinsic magnetic field of the sample. A change in the asymmetry function might indicate a phase transition; however, these changes can be very subtle, and existing methods of analyzing the data require knowledge about the specific physics of the material. PCA is an unsupervised ML tool, which means that no assumption about the input data is required, yet we found that it still can be successfully applied to asymmetry curves, and the indications of phase transitions can be recovered. The method was applied to a range of magnetic materials with different underlying physics. We discovered that performing PCA on all those materials simultaneously can have a positive effect on the clarity of phase transition indicators and can also improve the detection of the most important variations of asymmetry functions. For this joint PCA we introduce a simple way to track the contributions from different materials for a more meaningful analysis.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Physics and Astronomy
Additional Information: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.
Publisher: IOP Publishing
ISSN: 1742-6588
Date of First Compliant Deposit: 12 May 2022
Date of Acceptance: 23 December 2021
Last Modified: 17 May 2022 14:01
URI: https://orca.cardiff.ac.uk/id/eprint/149716

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