Until now, it is still difficult to identify different kinds of celestial bodies depending on their spectra, because it needs a lot of astronomers’ manual work of measuring, marking and identifying, which is generally very hard and time-consuming. And with the exploding spectral data from all kinds of telescopes, it is becoming more and more urgent to find a thoroughly automatic way to deal with such a kind of problem. In fact, when we change our viewpoint, we can find that it is a traditional problem in pattern recognition field when considering the whole process of dealing with spectral signals: filtering noises, extracting features, constructing classifiers, etc.
The main purpose for automatic classification and recognition of spectra in LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) project is to identify a celestial body’s type only based on its spectrum. For this purpose, one of the key steps is to establish a good model to describe all kinds of spectra and thus it will be available to construct some excellent classifiers.
In this paper, we present a novel describing language to represent spectra. And then, based on the language, we use some algorithms to extract classifying rules from raw spectra datasets and then construct classifiers to identify spectra by using rough set method. Compared with other methods, our technique is more similar to man’s thinking way, and to some extent, efficient.
In this paper, we present a novel technique for redshift identification. Redshift is a key parameter of celestial spectrums. In the literature, there are few reports on redshift identification due to either no many people working on the problem or perhaps industrial confidentiality. Our technique is a pseudo-triangle technique. It consists of the following three major steps: firstly, the 3 wavelengths corresponding to the 3 highest intensity values of an unknown spectrum are selected to construct a pseudo-triangle, and the largest angle of this triangle is calculated which is independent of redshift value. Secondly, the obtained angle is used as an index to retrieve the corresponding 3 model wavelengths via a pre-calculated look-up-table, which is composed of all the combinations of all the feature wavelengths of the model spectrum. And finally based on the 3 corresponding wavelengths, the corresponding redshift value is derived. The main characteristic of our technique is its simplicity and efficiency, which is demonstrated by experiments on simulated data as well as on real celestial spectrums. It is shown that the correct identification rate can reach as high as 86.7%. Taking into account the high noisy nature of celestial spectrums, such a result is considered a good one.
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