# Automatic Threshold Prediction of Photoelectron Yield Spectroscopy (PYS) by Machine Learning 

https://mdr.nims.go.jp/datasets/8baaff7b-6cba-492f-afcf-455c572d47b3

## Download

- [Readme.md](https://mdr.nims.go.jp/filesets/8289f2a3-e689-408b-9cad-65b8a0892d26/download)
- [requirements.txt](https://mdr.nims.go.jp/filesets/8812aa91-c7bd-4ce2-885d-0a91e2b813fd/download)
- [_最終稿版_機械学習による光電子収量分光PYSスペクトルの自動閾値予測200629.pdf](https://mdr.nims.go.jp/filesets/60f0531a-7bf0-424b-b786-b339ba482cef/download)
- [pys_mdr.zip](https://mdr.nims.go.jp/filesets/48a30428-465c-4d0e-b99d-9a87604c86e2/download)

## Id

8baaff7b-6cba-492f-afcf-455c572d47b3

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:23:59.077264Z

## Updated at

2024-01-05T13:12:44.529708Z

## Published at

2021-08-12T16:20:02.199482Z

## Doi

https://doi.org/10.48505/nims.1440

## First published url

https://doi.org/10.1380/vss.63.270

## Date published

2020-06-10

## Recorded date published

2020

## Resource type

journal_article

## Manuscript type

authors_original

## Collection



## Title

- title: 'Automatic Threshold Prediction of Photoelectron Yield Spectroscopy (PYS)
    by Machine Learning '
  title_type: original
  lang: en

## Description

- description: The prediction performance of the automatic threshold estimation of
    photoelectron yield spectroscopy using machine learning and least squares regression
    (fitting) was verified using 82 measured data. The correct answer rate was greater
    than 80％ for machine learning and less than 50％ for fitting, within an error range
    of ±0.3 eV with respect to the correct answer (the result of human spectrum analysis).
    To further improve the correct answer rate, it is necessary to change the energy
    range and energy step of the measured data because increase in the nonlinear intensity
    at the highenergy side of the spectrum is considered to affect the automatic estimation.
    The estimation did not change with changes in the energy step of the data. However,
    when the energy range in the data was changed, the estimation improved. If the
    threshold is expected to be less than 6.0 eV, the prediction is improved by using
    an energy range of 4.2–6.2 eV.
  description_type: abstract
  lang: en

## Creator

- name: YAGYU, Shinjiro
  role: author
  orcid: https://orcid.org/0000-0002-9825-5719
- name: YOSHITAKE, Michiko
  role: author
- name: CHIKYOW, Toyohiro
  role: author
  orcid: https://orcid.org/0000-0003-3860-4806
- name: NAGATA, Takahiro
  role: author
  orcid: https://orcid.org/0000-0002-8591-2943

## Contact agent



## Publisher

organization: The Japan Society of Vacuum and Surface Science

## Managing organization



## Keyword

- subject: threshold
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: photoelectron yield spectroscopy
  schema: not_defined

## Rights



## Other identifier(s)



## Data origin



## Embargo



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## Conference



## Related item



## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 8289f2a3-e689-408b-9cad-65b8a0892d26
  filename: Readme.md
  content_type: text/markdown
  size: 3438
  md5: c34b09a480327720a0b8fd5decab9b91
- id: 8812aa91-c7bd-4ce2-885d-0a91e2b813fd
  filename: requirements.txt
  content_type: text/plain
  size: 636
  md5: d8ab2dd941edda281873d9ef8c6b4159
- id: 60f0531a-7bf0-424b-b786-b339ba482cef
  filename: _最終稿版_機械学習による光電子収量分光PYSスペクトルの自動閾値予測200629.pdf
  content_type: application/pdf
  size: 1087325
  md5: 02e7bf152fc035979939ad5ca56df866
- id: 48a30428-465c-4d0e-b99d-9a87604c86e2
  filename: pys_mdr.zip
  content_type: application/zip
  size: 86807812
  md5: 03f488953edb22dc49f9a6b3f38012ae

## Thumbnail

fileset_id: 60f0531a-7bf0-424b-b786-b339ba482cef
filename: _最終稿版_機械学習による光電子収量分光PYSスペクトルの自動閾値予測200629.pdf