A scaling relation between merger rate of galaxies and their close pair count

We study how to measure the galaxy merger rate from the observed close pair count. Using a high-resolution N-body/SPH cosmological simulation, we find an accurate scaling relation between galaxy pair counts and merger rates down to a stellar mass ratio of about 1:30. The relation explicitly accounts for the dependence on redshift (or time), on pair separation, and on mass of the two galaxies in a pair. With this relation, one can easily obtain the mean merger timescale for a close pair of galaxies. The use of virial masses, instead of stellar masses, is motivated by the fact that the dynamical friction time scale is mainly determined by the dark matter surrounding central and satellite galaxies. This fact can also minimize the error induced by uncertainties in modeling star formation in the simulation. Since the virial mass can be read from the well-established relation between the virial masses and the stellar masses in observation, our scaling relation can be easily applied to observations to obtain the merger rate and merger time scale. For major merger pairs (1:1-1:4) of galaxies above a stellar mass of 4*10^10 M_sun/h at z=0.1, it takes about 0.31 Gyr to merge for pairs within a projected distance of 20 kpc/h with stellar mass ratio of 1:1, while the time taken goes up to 1.6 Gyr for mergers with stellar mass ratio of 1:4. Our results indicate that a single timescale usually used in literature is not accurate to describe mergers with the stellar mass ratio spanning even a narrow range from 1:1 to 1:4.Comment: accepted for publication in Ap

Sampling Artifact in Volume Weighted Velocity Measurement.--- II. Detection in simulations and comparison with theoretical modelling

Measuring the volume weighted velocity power spectrum suffers from a severe systematic error, due to imperfect sampling of the velocity field from inhomogeneous distribution of dark matter particles/halos in simulations or galaxies with velocity measurement. This "sampling artifact" depends on both the mean particle number density $\bar{n}_P$ and the intrinsic large scale structure (LSS) fluctuation in the particle distribution. (1) We report robust detection of this sampling artifact in N-body simulations. It causes $\sim 12$% underestimation of the velocity power spectrum at $k=0.1$h/Mpc for samples with $\bar{n}_P=6\times10^{-3}$ (Mpc/h)$^{-3}$. This systematic underestimation increases with decreasing $\bar{n}_P$ and increasing $k$. Its dependence on the intrinsic LSS fluctuations is also robustly detected. (2) All these findings are expected by our theoretical modelling in paper I \cite{Zhang14}. In particular, the leading order theoretical approximation agrees quantitatively well with simulation result for $\bar{n}_P\gtrsim6\times 10^{-4}$(Mpc/h)$^{-3}$. Furthermore, we provide an ansatz to take high order terms into account. It improves the model accuracy to $\lesssim1$% at $k\lesssim0.1$h/Mpc over 3 orders of magnitude in $\bar{n}_P$ and over typical LSS clustering from $z=0$ to $z=2$. (3) The sampling artifact is determined by the deflection ${\bf D}$ field, which is straightforwardly available in both simulations and data of galaxy velocity. Hence the sampling artifact in the velocity power spectrum measurement can be self-calibrated within our framework. By applying such self-calibration in simulations, it becomes promising to determine the {\it real} large scale velocity bias of $10^{13}M_\odot$ halos with $\sim 1$% accuracy, and that of lower mass halos by better accuracy. ...[abridged]Comment: 11 pages, 11 figures. More arguments added, match the PRD accepted versio

The Growth and Structure of Dark Matter Haloes

In this paper, we analyse in detail the mass-accretion histories and structural properties of dark haloes in high-resolution N-body simulations. Modeling the density distribution in individual haloes with the NFW profile, we find, for all main progenitors of a given halo, there is a tight correlation between its inner scale radius $r_s$ and the mass within it, $M_s$, which is the basic reason why halo structural properties are closely related to their mass-accretion histories. This correlation can be used to predict accurately the structural properties of a dark halo at any time from its mass-accretion history. We also test our model with a large sample of GIF haloes. The build-up of dark haloes in CDM models generally consists of an early phase of fast accretion and a late phase of slow accretion [where $M_h$ increases with time approximately as the expansion rate]. These two phases are separated at a time when the halo concentration parameter $c\sim 4$. Haloes in the two accretion phases show systematically different properties, for example, the circular velocity $v_h$ increases rapidly with time in the fast accretion phase but remain almost constant in the slow accretion phase,the inner properties of a halo, such as $r_s$ and $M_s$ increase rapidly with time in the fast accretion phase but change only slowly in the slow accretion phase. The potential well associated with a halo is built up mainly in the fast accretion phase, even though a large amount of mass (over 10 times) can be accreted in the slow accretion phase. We discuss our results in connection to the formation of dark haloes and galaxies in hierarchical models.Comment: 26 pages, including 10 figures. v2: some conceptual changes. Accepted for publication in MNRA

Kriging Interpolating Cosmic Velocity Field

[abridged] Volume-weighted statistics of large scale peculiar velocity is preferred by peculiar velocity cosmology, since it is free of uncertainties of galaxy density bias entangled in mass-weighted statistics. However, measuring the volume-weighted velocity statistics from galaxy (halo/simulation particle) velocity data is challenging. For the first time, we apply the Kriging interpolation to obtain the volume-weighted velocity field. Kriging is a minimum variance estimator. It predicts the most likely velocity for each place based on the velocity at other places. We test the performance of Kriging quantified by the E-mode velocity power spectrum from simulations. Dependences on the variogram prior used in Kriging, the number $n_k$ of the nearby particles to interpolate and the density $n_P$ of the observed sample are investigated. First, we find that Kriging induces $1\%$ and $3\%$ systematics at $k\sim 0.1h{\rm Mpc}^{-1}$ when $n_P\sim 6\times 10^{-2} ({\rm Mpc}/h)^{-3}$ and $n_P\sim 6\times 10^{-3} ({\rm Mpc}/h)^{-3}$, respectively. The deviation increases for decreasing $n_P$ and increasing $k$. When $n_P\lesssim 6\times 10^{-4} ({\rm Mpc}/h)^{-3}$, a smoothing effect dominates small scales, causing significant underestimation of the velocity power spectrum. Second, increasing $n_k$ helps to recover small scale power. However, for $n_P\lesssim 6\times 10^{-4} ({\rm Mpc}/h)^{-3}$ cases, the recovery is limited. Finally, Kriging is more sensitive to the variogram prior for lower sample density. The most straightforward application of Kriging on the cosmic velocity field does not show obvious advantages over the nearest-particle method (Zheng et al. 2013) and could not be directly applied to cosmology so far. However, whether potential improvements may be achieved by more delicate versions of Kriging is worth further investigation.Comment: 11 pages, 5 figures, published in PR