Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data

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 th...

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Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Han, Peng, Gao, Xiang, He, Yu, Li, Yiwei, Ji, Yuchen, Liu, Chuhang, Ekahana, Sandy A, Ding Pei, Liu, Zhongkai, Shen, Zhixun, Chen, Yulin
Format: Article
Language:English
Subjects:
Artificial neural networks
Data analysis
Experiments
Feature extraction
Inverse problems
Neural networks
Physics
Signal to noise ratio
Online Access:Get full text
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Summary: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 underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.
ISSN:2331-8422
DOI:10.48550/arxiv.2001.10908