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J. S. Warren, J. P. Hughes (Rutgers University)
We present a technique, Principal Component Analysis (PCA), for identifying X-ray spectral variations with position in supernova remnants (SNRs). PCA is a robust, unbiased statistical technique that allows us to identify and characterize X-ray spectra extracted from distinct spatial regions across the entire image of an SNR. We have applied PCA to Chandra ACIS observations of four Galactic remnants: Cassiopeia A, Tycho, SN1006, and G292.0+1.8. Our technique allows us to easily distinguish regions with line emission from those that are featureless and to further characterize line-rich regions according to their relative line strengths. In addition to confirming existing spectral variations in these remnants (e.g., localized regions with non-thermal power-law spectra, Fe-rich vs. Si-rich regions), we have also uncovered new spectral variations that have not been previously noted. In this presentation we provide details on the specific application of PCA to Chandra ACIS data. This powerful technique is an important new tool in our quest to understand the X-ray emission from SNRs.
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Bulletin of the American Astronomical Society, 36 #3
© 2004. The American Astronomical Soceity.