Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery

2023/02/18 by Guo, Yueming, Miao, Hu, Zou, Qiang +5
#Data Analysis #FOS: Physical sciences #Statistics and Probability (physics.data-an) #Superconductivity (cond-mat.supr-con)

paper · doi:10.48550/arxiv.2302.09337

Abstract

Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to isolate the intrinsic electronic states from extrinsic effects like subsurface defects and disorders. We combine STM/S with unsupervised machine learning to develop a method for screening out the vortices pinned by embedded disorder in Fe-based superconductors. The approach provides an unbiased way to reveal intrinsic vortex-core states and may address puzzles on Majorana zero modes.

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