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Machine learning approach to muon spectroscopy analysis.

Authors :
Tula T
Möller G
Quintanilla J
Giblin SR
Hillier AD
McCabe EE
Ramos S
Barker DS
Gibson S
Source :
Journal of physics. Condensed matter : an Institute of Physics journal [J Phys Condens Matter] 2021 Apr 26; Vol. 33 (19). Date of Electronic Publication: 2021 Apr 26.
Publication Year :
2021

Abstract

In recent years, artificial intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised machine learning (ML) algorithm called principal component analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase transitions in various materials. The measured quantity in muon spectroscopy is an asymmetry function, which may hold information about the distribution of the intrinsic magnetic field in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions-measured at different temperatures-might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right fitting function requires knowledge about the underlying physics of the probed material. Conversely, PCA focuses on small differences in the asymmetry curves and works without any prior assumptions about the studied samples. We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known. Additionally, we found out that our ML technique seems to work best with large numbers of measurements, regardless of whether the algorithm takes data only for a single material or whether the analysis is performed simultaneously for many materials with different physical properties.<br /> (© 2021 IOP Publishing Ltd.)

Details

Language :
English
ISSN :
1361-648X
Volume :
33
Issue :
19
Database :
MEDLINE
Journal :
Journal of physics. Condensed matter : an Institute of Physics journal
Publication Type :
Academic Journal
Accession number :
33545697
Full Text :
https://doi.org/10.1088/1361-648X/abe39e