A novel theory of the magnetostriction mechanism using topological data analysis

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Throughout last year, I have obtained a Kerr microscope image dataset of single crystal Fe-Ga alloy, from others collaborators. Working in cooperation with other colleagues, the data has been pre-processed to remove noise and scratches on the surface of the sample in the image data. The pre-processing has been done by a combination of robust principal component analysis (PCA) and singular value decomposition. For the analysis, I have been able to use unsupervised machine learning to separate the contributions of magnetization and magnetostriction based solely on the image data. I used a combination of PCA and fast Fourier transformation to extract the main features of the image data and connected to the physical parameters. I have observed that PCA could effectively distinguish the movements and types of domain walls, specifically 90 and 180-degree domains. By observing the PCA decomposition, I could observe that the first component (PC1) has a directly correlation with the 180-degree domain walls, while the second component could obtain the information from the 180 and 90-degree domain wall. In conclusion, I could observe and assign physical meaning to PCA features for experimental image data. Also, this results shows that multi-physics can be analyzed by the developing method.