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Data-mining Based Expert Platform for the Spectral Inspection

Published online by Cambridge University Press:  01 July 2015

Haijun Tian
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012. China Three Gorges University, Yichang, 443002. Email: [email protected]
Yang Xu
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
Yang Tu
Affiliation:
China Three Gorges University, Yichang, 443002. Email: [email protected]
Yanxia Zhang
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
Yongheng Zhao
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
Guohong Lei
Affiliation:
China Three Gorges University, Yichang, 443002. Email: [email protected]
Boliang He
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
Chenzhou Cui
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
Xuelei Chen
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012.
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Abstract

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We propose and preliminarily implement a data-mining based platform to assist experts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and it is VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2015 

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