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Proceedings Paper

Automated classification of x-ray sources in stellar clusters
Author(s): Susan M. Hojnacki; Joel H. Kastner
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Paper Abstract

The Chandra X-ray Observatory (CXO) is generating a tremendous amount of multi-dimensional X-ray data of exceptional quality. Currently, astronomers analyze these data one X-ray source at a time, via model-fitting techniques, to determine source physical conditions. More efficient methods of spectral and temporal classification would greatly benefit analysis of observations of rich fields of X-ray sources, such as stellar clusters. A combination of techniques from the fields of multivariate statistics and pattern recognition may provide new insight into, as well as an improvement in the speed and accuracy of, the classification of stellar X-ray spectra. We are adapting and applying such techniques, in the context of analysis of CXO and X-ray Multi-Mirror Mission (XMM-Newton) imaging spectroscopy of star formation regions, to group pre-main-sequence X-ray sources into clusters based on spectral attributes. An automated spectral classification technique for the Orion Nebula Cluster (ONC) population of greater than 1000 X-ray emitting young stars has been developed. As an initial test of the algorithm, deep CXO images of the ONC were analyzed. Clustering results are being compared with known optical, infrared, and radio properties of the young stellar population of the ONC, to assess the algorithm's ability to identify groups of sources that share common attributes.

Paper Details

Date Published: 16 September 2004
PDF: 9 pages
Proc. SPIE 5493, Optimizing Scientific Return for Astronomy through Information Technologies, (16 September 2004); doi: 10.1117/12.550967
Show Author Affiliations
Susan M. Hojnacki, Rochester Institute of Technology (United States)
Joel H. Kastner, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 5493:
Optimizing Scientific Return for Astronomy through Information Technologies
Peter J. Quinn; Alan Bridger, Editor(s)

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