An unsupervised, ensemble clustering algorithm: A new approach for classification of X-ray sources

S. M. Hojnacki, G. Micela, S. M. LaLonde, E. D. Feigelson, J. H. Kastner

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)350-360
Number of pages11
JournalStatistical Methodology
Volume5
Issue number4
DOIs
StatePublished - Jul 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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