Gaussian Peaks and Clusters of Galaxies

Abstract

(shortened) We develop and test a method to compute mass and auto-correlation functions of rich clusters of galaxies from linear density fluctuations, based on the formalism of Gaussian peaks (Bardeen et al 1986). The essential, new ingredient in the current approach is a simultaneous and unique fixture of the size of the smoothing window for the density field, $r_f$, and the critical height for collapse of a density peak, ${\delta }_c$, for a given cluster mass (enclosed within the sphere of a given radius rather than the virial radius, which is hard to measure observationally). Of these two parameters, $r_f$ depends on both the mass of the cluster in question and $\Omega$, whereas ${\delta }_c$ is a function of only $\Omega$ and $\Lambda$. These two parameters have formerly been treated as adjustable and approximate parameters. Thus, for the first time, the Gaussian Peak Method (GPM) becomes unambiguous, and more importantly, accurate, as is shown here. We apply this method to constrain all variants of the Gaussian cold dark matter (CDM) cosmological model using the observed abundance of local rich clusters of galaxies and the microwave background temperature fluctuations observed by COBE. The combined constraint fixes the power spectrum of any model to $\sim 10$ accuracy in both the shape and overall amplitude. To set the context for analyzing CDM models, we choose six representative models of current interest, including an ${\Omega }_0=1$ tilted cold dark matter model, a mixed hot and cold dark matter model with 20% mass in neutrinos, two lower density open models with ${\Omega }_0=0.25$ and ${\Omega }_0=0.40$, and two lower density flat models with $({\Omega }_0=0.25,{\Lambda }_0=0.75)$ and $({\Omega }_0=0.40,{\Lambda }_0=0.60)$. This suite of CDM models

ApJ in press (1998), two-column emulateapj, 28 pages including figs

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