# Self-Energy Spectroscopy and Artificial Neural Network

https://mdr.nims.go.jp/datasets/3431154c-3506-4179-bfca-e7ebc44aa0bd

## File

- [jpsj.94.031005.pdf](https://mdr.nims.go.jp/filesets/9df24ec1-5f2b-4b64-aa39-108848433f06/download) ([Detail](https://mdr.nims.go.jp/filesets/9df24ec1-5f2b-4b64-aa39-108848433f06.md))

## Id

3431154c-3506-4179-bfca-e7ebc44aa0bd

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-02-26T07:51:59.138484Z

## Updated at

2025-02-27T03:30:42.157654Z

## Published at

2025-02-27T03:30:42.273550Z

## Doi



## First published url

https://doi.org/10.7566/jpsj.94.031005

## Date published

2025-03-15

## Recorded date published

2025-3-15

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Self-Energy Spectroscopy and Artificial Neural Network
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Youhei Yamaji
  role: author
  orcid: https://orcid.org/0000-0002-4055-8792

## Contact agent



## Publisher

organization: Physical Society of Japan

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## Keyword

- subject: machine learning
  schema: not_defined
- subject: quantum materials
  schema: not_defined
- subject: photoemission spectroscopy
  schema: not_defined
- subject: high-temperature superconductivity
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Journal

- title: Journal of the Physical Society of Japan
  issn: '00319015'
  volume: '94'
  issue: '3'

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## Related item



## Funding

- identifier: JPMXP1020230410
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: 23H04524
  funder_name: Japan Society for the Promotion of Science

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## Fileset

- id: 9df24ec1-5f2b-4b64-aa39-108848433f06
  filename: jpsj.94.031005.pdf
  content_type: application/pdf
  size: 2075294
  md5: 8d2bc804746325189b482443806916c0

## Thumbnail

fileset_id: 9df24ec1-5f2b-4b64-aa39-108848433f06
filename: jpsj.94.031005.pdf