Influence of surface brightness and size constraints on galaxy survey counts and evolution
- Understanding the formation and evolution of structure in the universe requires accurate quantification of populations of galaxies. The traditional single-variable luminosity function that has been used to quantify galaxy populations has been found lacking because the galaxy luminosity function has major discrepancies between surveys. Many authors have sought to resolve these discrepancies by finding correlations with other galaxy properties. Thus, galaxy statistics are increasingly being recast as multivariate functions. Here we make the case for one such popular function -- a bivariate brightness function in luminosity and surface brightness. To recover actual galaxy counts survey limits must be consistently applied in the same quantities used to construct the statistic. In particular, surface brightness is a natural second variable to use because the criteria used to select objects from an astronomical image are typically surface brightness and size. We construct a model for predicting the number of galaxies observed as a function of redshift and morphological type, excluding effects of evolution. We illustrate the effect upon galaxy counts due to the inclusion of surface brightness bias compared to the inclusion of the cosmological K-correction, the robustness of the method against uncertainty in the bivariate brightness function parameters, and the sensitivity of the expected galaxy counts to survey limits. Comparing observation against our no-evolution model can then yield evolution effects as a function of galaxy morphology -- if we can reliably classify galaxy morphology for large surveys. We focus on methods that can be applied directly to existing photometric catalogs, using the GOODS ACS data set to illustrate our methods. We first consider an attempt to directly extract galaxy morphology by means of a "concentration index'' calculated from aperture photometry. We find this method lacking in discriminatory power and dominated by significant error both in the base aperture photometry and in the fits derived thereof. This classification scheme we then compare to one derived from using a new neural network technique, Spatial Relational Learning. This algorithm not only produces error-free learning based upon the most closely biological neural model to date, but also allows for the extraction of classification rules rather than mere use as a "black box.'' We find that a simple two-variable classification scheme of b-v color and a best-fit Gaussian FWHM parameter, already a standard output of the most commonly used photometric extraction pipeline software, provides highly reasonable morphological classification. With this in hand, we can consider the task of extracting redshift distributions by morphological type from existing photometric catalogs to compare against our no-evolution models. In the case of the GOODS ACS data, we find evidence of significantly faster and more recent evolution among early-type, elliptical galaxies than late-type, disk galaxies. We then turn to consider the Sloan Digital Sky Survey; aside from its massive statistical potential, we consider Sloan because it also gives us the opportunity to explicate the use of non-isophotal detection limits and their effect upon the procedure we have laid out thus far. However, we show that even beyond the problem of shallower redshift coverage (and uncertainty in photometric redshifts), the SDSS is not well-suited for an analysis of this type. A galaxy survey must have a reasonably well-defined set of characteristic survey limits drawn from the actual detection pipeline in order to be useful for our no-evolution model. Nevertheless, even a relatively crude isophotal approximation to survey detection limits illustrates both the robustness of our approach and the pitfalls of morphology-sensitive selection criteria for object detection. Finally, in reviewing the history of hierarchical structure formation theory, our results suggest that the differing evolution epochs observed in the GOODS data argue away from the classical concept of one morphological type of galaxies evolving into another (e.g. via mergers) and more toward morphology-dependent physical processes. In particular we find for disk galaxies, long formation times for the thin disk structure, and for elliptical galaxies, star formation rate suppression and morphological shaping due to AGN-driven downsizing.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|2009, c2010; 2009
|Dorris, Michael James
|Stanford University, Department of Physics
|Wagoner, Robert V
|Wagoner, Robert V
|Statement of responsibility
|Submitted to the Department of Physics.
|Thesis (Ph.D.)--Stanford University, 2010.
- © 2010 by Michael James Dorris
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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