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The Chandra megasecond observation of Cas A allows for a spectrum to be
obtained from every pixel of the CCD. The question of where to begin the
analysis and the corresponding answers that can be gleaned from the data
are potentially overwhelming. One way to get a handle on the data is with
a principal component analysis (PCA). PCA is a mathematical technique used
to reduce the dimensionality of a dataset. It compares spectra from
different regions in an SNR and identifies those that vary significantly
from the average spectrum in some way. This technique has already proven
successful in the study of Tycho$apos;s SNR. I will discuss the method of
PCA and how it is applied to SNRs, specifically the Cas A megasecond
data. I will present some preliminary results, with an eye toward areas
that warrant further investigation.
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