Cluster Cosmology Without Cluster Finding

Kavli Affiliate: Risa H. Wechsler

| First 5 Authors: Enia Xhakaj, Alexie Leauthaud, Johannes Lange, Elisabeth Krause, Andrew Hearin

| Summary:

We propose that observations of super-massive galaxies contain cosmological
constraining power similar to conventional cluster cosmology, and we provide
promising indications that the associated systematic errors are comparably
easier to control. We consider a fiducial spectroscopic and stellar mass
complete sample of galaxies drawn from the Dark Energy Spectroscopic Survey
(DESI) and forecast how constraints on Omega_m-sigma_8 from this sample will
compare with those from number counts of clusters based on richness. At fixed
number density, we find that massive galaxies offer similar constraints to
galaxy clusters. However, a mass-complete galaxy sample from DESI has the
potential to probe lower halo masses than standard optical cluster samples
(which are typically limited to richness above 20 and halo mass above 10^13.5);
additionally, it is straightforward to cleanly measure projected galaxy
clustering for such a DESI sample, which we show can substantially improve the
constraining power on Omega_m. We also compare the constraining power of
stellar mass-limited samples to those from larger but mass-incomplete samples
(e.g., the DESI Bright Galaxy Survey, BGS, Sample); relative to a lower number
density stellar mass-limited samples, we find that a BGS-like sample improves
statistical constraints by 60% for Omega_m and 40% for sigma_8, but this uses
small scale information which will be harder to model for BGS. Our initial
assessment of the systematics associated with supermassive galaxy cosmology
yields promising results. The proposed samples have a 10% satellite fraction,
but we show that cosmological constraints may be robust to the impact of
satellites. These findings motivate future work to realize the potential of
super-massive galaxies to probe lower halo masses than richness-based clusters
and to avoid persistent systematics associated with optical cluster finding.

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