Astronomy and Astrophysics – Astronomy
Scientific paper
Aug 1996
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996phdt........36t&link_type=abstract
PhD Dissertation, University of Wyoming, Laramie, WY
Astronomy and Astrophysics
Astronomy
1
Extra-Galactic, M81, M51, Ngc 6504, Pca, Spectra, Photometry
Scientific paper
The advent of new technologies, such as CCD cameras and optical fibers, has increased the spectral range, spectral resolution, and signal-to-noise ratio of measurements from extra-galactic objects. We expect this trend to continue. The use of traditional astronomical analysis techniques such as color-color plots, will be inadequate to handle the large amount of high quality spectral data that will result There are already two multivariate analysis techniques used in many fields that can easily reduce multi- dimensional data, such as spatially resolved high resolution spectra. These techniques are Principal Component Analysis and linear spectral mixture modeling. I explore the usefulness of these techniques by analyzing several different types of spectral data, including: integrated spectra, spectrophotometric imaging, and slit spectra. Each type of spectra holds clues to galaxy composition. The integrated spectra of several galaxies allows one to estimate stellar mass-to-light ratios, star formation histories, and stellar compositions. I develop a narrow-band photometric technique that can be applied to distant unresolved galaxies, thus allowing us to see clues into the evolution of galaxies The analysis of spectrophotometric imagery of NGC 6504 allows one to determine four spectrally distinct spatially resolved components. We find that the components of NGC 6504 are the thin disk, the dust lane, and a young disk. The analysis of slit spectra of the M81 and M51 nuclei is the first time that multivariate techniques have been used to analyze spatially resolve8d spectra into component populations. I find evidence for continuing star formation in the M51 nucleus. I find that spectral mixture modeling has the greatest success when data points are not completely independent, ( e.g. integrated galaxy spectra), but are spectrally correlated with neighboring data points. and multispectral images (e.g. slit spectra). While I did not use 3 dimensional spectroscopy in this research, I believe that the use of this type of data optimize the use of spectral mixture modeling
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