期刊
ASTROPHYSICAL JOURNAL
卷 592, 期 1, 页码 1-16出版社
IOP Publishing Ltd
DOI: 10.1086/375717
关键词
cosmological parameters; cosmology : theory; galaxies : evolution; galaxies : luminosity function, mass function
This paper aims to connect the theory of relativistic cosmology number counts with the astronomical data, practice, and theory behind the galaxy luminosity function (LF). We treat galaxies as the building blocks of the universe but ignore most aspects of their internal structures by considering them as point sources. However, we do consider general morphological types in order to use data from galaxy redshift surveys, where some kind of morphological classification is adopted. We start with a general relativistic treatment for a general spacetime, not just for Friedmann-Lemaitre-Robertson-Walker, of number counts, and then we link the derived expressions with the LF definition adopted in observational cosmology. Then equations for differential number counts, the related relativistic density per source, and observed and total relativistic energy densities of the universe, as well as other related quantities, are written in terms of the luminosity and selection functions. As an example of how these theoretical/observational relationships can be used, we apply them to test the LF parameters determined from the CNOC2 galaxy redshift survey, for consistency with the Einstein-de Sitter (EdS) cosmology, which they assume, for intermediate redshifts. We conclude that there is a general consistency for the tests we carried out, namely, for both the observed relativistic mass-energy density and the observed relativistic mass-energy density per source, which is equivalent to differential number counts, in an EdS universe. In addition, we find clear evidence of a large amount of hidden mass, as has been obvious from many earlier investigations. At the same time, we find that the CNOC2 LF gives differential galaxy counts somewhat above the EdS predictions, indicating that this survey observes more galaxies at 0.1 less than or similar to z less than or similar to 0.4 than the model's predictions.
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