Schedule Feb 10, 2006
A Principal Component Analysis Technique Applied to Cas A
Jessica Warren (Rutgers)

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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