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Machine learning approach to muon spectroscopy analysis

Tula, Tymoteusz; Möller, Gunnar; Quintanilla, Jorge; Giblin, Sean; Hillier, A; McCabe, Emma; Ramos, Silvia; Barker, Dylan; Gibson, Stuart

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Authors

Tymoteusz Tula

Gunnar Möller

Jorge Quintanilla

Sean Giblin

A Hillier

Silvia Ramos

Dylan Barker

Stuart Gibson



Abstract

In recent years, articial 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. Specically, 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 eld in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions { measured at dierent temperatures { might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right tting function requires knowledge about the underlying physics of the probed material. Conversely, principal component analysis focuses on small dierences 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 dierent physical properties.

Journal Article Type Article
Acceptance Date Feb 5, 2021
Online Publication Date Apr 26, 2021
Publication Date May 12, 2021
Deposit Date Jan 14, 2021
Publicly Available Date Nov 10, 2022
Journal Journal of Physics: Condensed Matter
Print ISSN 0953-8984
Electronic ISSN 1361-648X
Publisher IOP Publishing
Peer Reviewed Peer Reviewed
Volume 33
Issue 19
Article Number 194002
DOI https://doi.org/10.1088/1361-648x/abe39e
Public URL https://durham-repository.worktribe.com/output/1247539

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License.
Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.






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