A Principal Component Analysis of X-ray Spectral Variations in Four Galactic SNRs

Details A Principal Component Analysis of X-ray Spectral Variations in Four Galactic SNRs A Principal Component Analysis of X-ray Spectral Variations in Four Galactic SNRs

Aug 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004head....8.2420w&link_type=abstract

American Astronomical Society, HEAD meeting #8, #24.20; Bulletin of the American Astronomical Society, Vol. 36, p.952

Astronomy and Astrophysics

Astronomy

Scientific paper

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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