A Principal Component Analysis Technique Applied to Cas A

Details A Principal Component Analysis Technique Applied to Cas A A Principal Component Analysis Technique Applied to Cas A

Feb 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006sgrb.confe..68w&link_type=abstract

Presented at the KITP Conference: Supernova and Gamma-Ray Burst Remnants, Feb 10, 2006, Kavli Institute for Theoretical Physics,

Physics

Scientific paper

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

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