Modelling large-scale halo bias using the bispectrum

Details Modelling large-scale halo bias using the bispectrum Modelling large-scale halo bias using the bispectrum

2011-09-15

arxiv.org/abs/1109.3458v3

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

23 pages, 7 figures; accepted for publication in MNRAS

Scientific paper

We study the relation between the halo and matter density fields -- commonly termed bias -- in the LCDM framework. In particular, we examine the local model of biasing at quadratic order in the matter density. This model is characterized by parameters b_1 and b_2. Using an ensemble of N-body simulations, we apply several statistical methods to estimate the parameters. We measure halo and matter fluctuations smoothed on various scales and find that the parameters vary with smoothing scale. We argue that, for real-space measurements, owing to the mixing of wavemodes, no scale can be found for which the parameters are independent of smoothing. However, this is not the case in Fourier space. We measure halo power spectra and construct estimates for an effective large-scale bias. We measure the configuration dependence of the halo bispectra B_hhh and reduced bispectra Q_hhh for very large-scale k-space triangles. From this we constrain b_1 and b_2. Using the lowest-order perturbation theory, we find that for B_hhh the best-fit parameters are in reasonable agreement with one another as the triangle scale is varied, but that the fits become poor as smaller scales are included. The same is true for Q_hhh. The best-fit parameters depend on the discreteness correction. This led us to consider halo-mass cross-bispectra. The results from these statistics support our earlier findings. We develop a test to explore the importance of missing higher-order terms in the models. We prove that low-order expansions are not able to correctly model the data, even on scales k_1~0.04 h/Mpc. If robust inferences are to be drawn from galaxy surveys, then accurate models for the full nonlinear matter bispectrum and trispectrum will be essential.

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