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The reliability of data storage and writing speed in advanced magnetic devices depend on drastic, complex changes in microscopic magnetic domain structures. However, it is extremely challenging to quantify these changes, limiting our understanding of magnetic phenomena. To tackle this, researchers developed, using machine learning and topology, an analysis method that quantifies the complexity of the magnetic domain structures, revealing hidden features of magnetization reversal that are hardly seen by human eyes.