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The analysis of spectroscopy data has played an important role in untangling the complex dynamics of many-body electrons in quantum materials and making their emergent properties understandable. Spectroscopy measurements provide us with the responses of the many-body electrons in materials when energy and momentum are injected. These responses have been analyzed using a single-particle picture augmented by self-energy, which quantifies the deviation from simple free fermion excitation. While the self-energy is not directly observed in spectroscopy, it has been extracted from the obtained spectra by solving inverse problems. Especially, for superconductors, the analysis of self-energy is a key to understanding the origin of the superconductivity. The recent rise of machine learning has served to update the self-energy analysis of spectroscopy data and opened a new avenue for understanding the entangled nature of manybody electrons. In this article, self-energy analysis using the flexibility of neural networks is reviewed and positioned in the research trajectory from Bardeen–Cooper–Schrieffer superconductors to copper-oxide high-temperature superconductors.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: machine learning, quantum materials, photoemission spectroscopy, high-temperature superconductivity

Date published: 2025-03-15

Publisher: Physical Society of Japan

Journal:

• Journal of the Physical Society of Japan (ISSN: 00319015) vol. 94 issue. 3

Funding:

• Ministry of Education, Culture, Sports, Science and Technology JPMXP1020230410
• Japan Society for the Promotion of Science 23H04524

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.7566/jpsj.94.031005

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Updated at: 2025-02-27 12:30:42 +0900

Published on MDR: 2025-02-27 12:30:42 +0900