An unsupervised, ensemble clustering algorithm: A new approach for classification of X-ray sources |
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Authors: | SM Hojnacki G Micela SM LaLonde ED Feigelson JH Kastner |
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Institution: | 1. Center for Imaging Science, Rochester Institute of Technology, 74 Lomb Memorial Drive, Rochester, NY 14623, United States;2. INAF, Osservatorio Astronomico di Palermo G. S. Vaiana, Piazza del Parlamento 1, 90134 Palermo, Italy;3. Department of Astronomy and Astrophysics, Pennsylvania State University, 525 Davey Laboratory, University Park, PA 16802, United States;4. Center for Quality and Applied Statistics, Rochester Institute of Technology, 98 Lomb Memorial Drive, Rochester, NY 14623, United States |
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Abstract: | A large volume of CCD X-ray spectra is being generated by the Chandra X-ray Observatory (Chandra) and XMM-Newton. Automated spectral analysis and classification methods can aid in sorting, characterizing, and classifying this large volume of CCD X-ray spectra in a non-parametric fashion, complementary to current parametric model fits. We have developed an algorithm that uses multivariate statistical techniques, including an ensemble clustering method, applied for the first time for X-ray spectral classification. The algorithm uses spectral data to group similar discrete sources of X-ray emission by placing the X-ray sources in a three-dimensional spectral sequence and then grouping the ordered sources into clusters based on their spectra. This new method can handle large quantities of data and operate independently of the requirement of spectral source models and a priori knowledge concerning the nature of the sources (i.e., young stars, interacting binaries, active galactic nuclei). We apply the method to Chandra imaging spectroscopy of the young stellar clusters in the Orion Nebula Cluster and the NGC 1333 star formation region. |
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Keywords: | Statistical methods Unsupervised clustering Nonparametric X-ray emission Young stellar clusters |
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