Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network
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Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network Emmanuel Bratsolis ´ D´epartement Traitement du Signal et des Images, Ecole Nationale Sup´erieure des T´el´ecommunications, 46 rue Barrault, 75013 Paris, France Email: [email protected] Section of Astrophysics, Astronomy and Mechanics, Department of Physics, University of Athens, 15784 Athens, Greece Email: [email protected] Received 28 May 2004; Revised 14 December 2004 Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN). We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC). Digitized photographic material has been used here. The method can also be used on CCD spectral images. Keywords and phrases: objective prism stellar spectra, classification, artificial neural network.
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INTRODUCTION
Large surveys are concerned with two things. The first is finding unusual objects. Once detected, these unusual objects must always be analyzed individually. The second one is to do statistics with large numbers of objects. In this case, we need an automated classification system. High-quality film copies of IIIa-J (broad blue-green band) plates, taken with the 1.2 m UK Schmidt Telescope in Australia, have been used. The spectral plates are with disper˚ sion of 2 440 A/mm at Hγ and spectral range from 3 200 to ˚ 5 400 A. The photographic material has been digitized at the Royal Observatory of Edinburgh using the SuperCOSMOS machine. Stellar classification with ANNs as a nonlinear technique has been used by many other researchers in the last decade [1, 2, 3, 4, 5]. These methods were utilized for different databases and different spectral dispersion images. In this work, we use wide-field images from the 1.2 m UK Schmidt Telescope in Australia with an objective prism P1. In this case, we can work directly on the image making detection, extraction, classification, and testing of population synthesis. The main contribution here is that there is
an automated method, useful to study the spatial distribution of stars (we have the stellar coordinates from the detection method) in groups with the same spectral type (from the classification method). It is useful in astrophysics because we can have a spatial distribution of stellar groups with the same age (grosso modo) and we can study them separetly (morphology, mixture of different populations, etc.). The final aim of this automated method is to study
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