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S.M. Hojnacki, J.H. Kastner, S.M. LaLonde (Rochester Institute of Technology), G. Micela (Osservatorio Astronomico G.S. Vaiana of Palermo), E.D. Feigelson (Pennsylvania State University)
The Chandra X-ray Observatory is producing images with outstanding spatial resolution using low-noise, fast-readout CCDs. Techniques than can objectively and efficiently classify X-ray sources in large images of rich fields of sources are needed to analyze the growing Chandra image archive. One such dataset, the Chandra Orion Ultradeep Project (COUP) dataset, was compiled from an 850 ks Chandra observation of the Orion Nebula Cluster (ONC). It represents the most sensitive and comprehensive description of X-ray emission from a pre-main sequence star cluster (Getman et al. 2005, ApJS, 160, 000). A statistical image processing algorithm has been developed that groups the ONC X-ray sources into classes based on their spectral attributes. The algorithm was applied to a subset of 444 of the 1616 X-ray sources detected in the COUP dataset, resulting in sets of distinct X-ray spectral classes. The X-ray class membership for each of the remaining approximately 1150 COUP sources was then predicted, using the X-ray class definitions obtained from running the algorithm on the initial training subset. Our results show that ONC and extragalactic X-ray sources can potentially be distinguished via this classification method. In addition, there are clear correlations between the softer X-ray spectral classes and classical optical spectral types in the cluster H-R diagram.
This research is supported by NASA under AISRP award number NNG04GQ07G and Chandra Award Number AR5-6004X issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for an on behalf of NASA under contract NAS8-03060.
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Bulletin of the American Astronomical Society, 37 #4
© 2005. The American Astronomical Soceity.