Mathematics – Logic
Scientific paper
May 1998
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998aas...192.2502b&link_type=abstract
American Astronomical Society, 192nd AAS Meeting, #25.02; Bulletin of the American Astronomical Society, Vol. 30, p.848
Mathematics
Logic
Scientific paper
Studies of the distribution and evolution of galaxies are of fundamental importance to modern cosmology; these studies, however, are hampered by the complexity of the competing effects of spectral and density evolution. Constructing a spectroscopic sample that is able to unambiguously disentangle these processes is excessively prohibitive. We demonstrate the advantages of adopting a statistical approach to understanding galaxy evolution. First, statistical distance indicators provide a redshift estimate that together with an estimate for the redshift error, allows for the calculation of astrophysically relevant statistics. Second, incorporating spectral classifications into the data allows the calculation of these tests to be done for different spectral types. Taken together, this approach shows a great deal of promise in disentangling the complicated effects of galaxy evolution. We extend and apply this alternative approach that relies on statistical estimates for both distance (zP) and spectral type to a deep multi-band dataset that was obtained for this exact purpose. These statistical estimates are extracted directly from the photometric data by capitalizing on the inherent relationships between flux, redshift, and spectral type. The number of galaxies contained in our sample is sufficient to construct the multivariate angular correlation function w(theta , zP, Type) and the multivariate luminosity function Phi (MAB, zP, Type) to zP ~ 1.0. With these measurements, we can begin to separate out the competing effects of density and spectral evolution, and place constraints on fundamental cosmological quantities.
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