Mock galaxy catalogs using the quick particle mesh method

Sophisticated analysis of modern large-scale structure surveys requires mock catalogs. Mock catalogs are used to optimize survey design, test reduction and analysis pipelines, make theoretical predictions for basic observables and propagate errors through complex analysis chains. We present a new method, which we call "quick particle mesh", for generating many large-volume, approximate mock catalogs at low computational cost. The method is based on using rapid, low-resolution particle mesh simulations that accurately reproduce the large-scale dark matter density field. Particles are sampled from the density field based on their local density such that they have N-point statistics nearly equivalent to the halos resolved in high-resolution simulations, creating a set of mock halos that can be populated using halo occupation methods to create galaxy mocks for a variety of possible target classes.Comment: 13 pages, 16 figures. Matches version accepted by MNRAS. Code available at http://github.com/mockFactor

Tests of redshift-space distortions models in configuration space for the analysis of the BOSS final data release

Observations of redshift-space distortions in spectroscopic galaxy surveys offer an attractive method for observing the build-up of cosmological structure, which depends both on the expansion rate of the Universe and our theory of gravity. In preparation for analysis of redshift-space distortions from the Baryon Oscillation Spectroscopic Survey (BOSS) final data release we compare a number of analytic and phenomenological `streaming' models, specified in configuration space, to mock catalogs derived in different ways from several N-body simulations. The galaxies in each mock catalog have properties similar to those of the higher redshift galaxies measured by BOSS but differ in the details of how small-scale velocities and halo occupancy are determined. We find that all of the analytic models fit the simulations over a limited range of scales while failing at small scales. We discuss which models are most robust and on which scales they return reliable estimates of the rate of growth of structure: we find that models based on some form of resummation can fit our N-body data for BOSS-like galaxies above $30\,h^{-1}$Mpc well enough to return unbiased parameter estimates.Comment: 12 pages, 11 figures, matches version accepted by MNRA

Spatial Clustering of Dark Matter Halos: Secondary Bias, Neighbor Bias, and the Influence of Massive Neighbors on Halo Properties

We explore the phenomenon commonly known as halo assembly bias, whereby dark matter halos of the same mass are found to be more or less clustered when a second halo property is considered, for halos in the mass range $3.7 \times 10^{11} \; h^{-1} \mathrm{M_{\odot}} - 5.0 \times 10^{13} \; h^{-1} \mathrm{M_{\odot}}$. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if halos are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clustering information of the halo population. The mean values of some halo properties depend on their halo's distance to a more massive neighbor. Halo samples selected by having high values of one of these properties therefore inherit a neighbor bias such that they are much more likely to be close to a much more massive neighbor. This neighbor bias largely accounts for the secondary bias seen in halos binned by mass and split by concentration or age. However, halos binned by other mass-like properties still show a secondary bias even when the neighbor bias is removed. The secondary bias of halos selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.Comment: 14 pages, LaTeX; minor revisions, and added references; results unchange