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Super resolution convolutional neural network for feature extraction in spectroscopic data

Abstract:

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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Publisher copy:
10.1063/1.5132586

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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Role:
Author
More by this author
Division:
MPLS
Department:
Physics
Role:
Author
More by this author
Division:
MPLS
Department:
Physics
Role:
Author
More by this author
Division:
MPLS
Department:
Physics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Role:
Author
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:
1089-7623
ISSN:
0034-6748
Keywords:
Pubs id:
1090849
Local pid:
pubs:1090849
Deposit date:
2020-03-04

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