Machine learning approach to muon spectroscopy analysis

• Published in: Journal of physics. Condensed matter Vol. 33; no. 19; pp. 194002 - 194012
• Main Authors: Tula, T, Möller, G, Quintanilla, J, Giblin, S R, Hillier, A D, McCabe, E E, Ramos, S, Barker, D S, Gibson, S
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 26.04.2021
• Subjects: identifying phase transitions; machine learning; muon spectroscopy; muon spin relaxation experiment; principal component analysis; time-reversal symmetry breaking superconductors; time-reversal symmetry breaking superconductors; muon spin relaxation experiment; machine learning; muon spectroscopy; principal component analysis; identifying phase transitions
• ISSN: 0953-8984; 1361-648X
• DOI: 10.1088/1361-648X/abe39e

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.