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Joint machine learning analysis of muon spectroscopy data from different materials
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.
Machine Learning approach to muon spectroscopy analysis
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.
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