Journal article
Super resolution convolutional neural network for feature extraction in spectroscopic data
- Abstract:
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Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, thus can be solved with a convolutional neural network (CNN). In addition, we show that the und...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 6.6MB)
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- Publisher copy:
- 10.1063/1.5132586
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Authors
Bibliographic Details
- Publisher:
- AIP Publishing Publisher's website
- Journal:
- Review of Scientific Instruments Journal website
- Volume:
- 91
- Issue:
- 2020
- Article number:
- 033905
- Publication date:
- 2020-03-12
- Acceptance date:
- 2020-02-20
- DOI:
- EISSN:
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1089-7623
- ISSN:
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0034-6748
Item Description
- Keywords:
- Pubs id:
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1090849
- Local pid:
- pubs:1090849
- Deposit date:
- 2020-03-04
Terms of use
- Copyright holder:
- Peng et al.
- Copyright date:
- 2020
- Rights statement:
- © 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- Notes:
- This is the accepted manuscript version of the article. The final version of the article will be available in a forthcoming edition of Review of Scientific Instruments.
- Licence:
- CC Attribution (CC BY)
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