Determination of the large scale volume weighted halo velocity bias in simulations

Zheng, Yi; Zhang, Pengjie; Jing, Y. P.

doi:10.1103/physrevd.91.123512

Cited by 30 publications

(36 citation statements)

References 24 publications

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“…This will then allow us to measure the real halo velocity bias, free of otherwise severe systematic error from the sampling artifact. We will present such measurements in [5], which belongs to our ongoing efforts toward understanding the velocity field, redshift shift space distortion, and velocity reconstruction in spectroscopic redshift surveys [12,24]. The current paper focuses on the sampling artifact in the gradient part of the velocity field.…”

Section: Self-calibration and Discussionmentioning

confidence: 99%

“…The existence of a sampling artifact in the volume weighted velocity power spectrum has been realized in simulations [5,12,13] for both the DM and halo velocity fields. These works found that its impact increases with decreasing number density.…”

Section: Introductionmentioning

confidence: 94%

“…The present paper (paper II) focuses on its numerical verification in order to improve our understanding of the sampling artifact. In [5] we will apply this improved understanding to correct the sampling artifact in the halo/galaxy velocity field and robustly measure the halo velocity bias.…”

Section: Introductionmentioning

confidence: 99%

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Sampling artifact in volume weighted velocity measurement. II. Detection in simulations and comparison with theoretical modeling

2015

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Measuring the volume weighted velocity power spectrum suffers from a severe systematic error due to imperfect sampling of the velocity field from the inhomogeneous distribution of dark matter particles/halos in simulations or galaxies with velocity measurement. This "sampling artifact" depends on both the mean particle number densityn 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 ∼12% underestimation of the velocity power spectrum at k ¼ 0.1 h=Mpc for samples withn P ¼ 6 × 10 −3 ðMpc=hÞ −3 . This systematic underestimation increases with decreasinḡ n P and increasing k. Its dependence on the intrinsic LSS fluctuations is also robustly detected.(2) All of these findings are expected based upon our theoretical modeling in paper I [P. Zhang, Y. Zheng, and Y. Jing, Sampling artifact in volume weighted velocity measurement. I. Theoretical modeling, arXiv:1405.7125.]. In particular, the leading order theoretical approximation agrees quantitatively well with the simulation result forn P ≳ 6 × 10 −4 ðMpc=hÞ −3 . Furthermore, we provide an ansatz to take high order terms into account. It improves the model accuracy to ≲1% at k ≲ 0.1 h=Mpc over 3 orders of magnitude inn P and over typical LSS clustering from z ¼ 0 to z ¼ 2.(3) The sampling artifact is determined by the deflection 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 is promising to determine the real large scale velocity bias of 10 13 M ⊙ halos with ∼1% accuracy, and that of lower mass halos with better accuracy. (4) In contrast to suppressing the velocity power spectrum at large scale, the sampling artifact causes an overestimation of the velocity dispersion. We prove that a correlation between the signal field (v) and the sampling field (D) is a major cause. This complexity, among other issues, is carefully investigated to further improve understanding of the sampling artifact.PHYSICAL REVIEW D 91, 043523 (2015) 1550-7998=2015=91(4)=043523 (13) 043523-1

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Section: Self-calibration and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

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Sampling artifact in volume weighted velocity measurement. II. Detection in simulations and comparison with theoretical modeling

2015

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“…In recent years many studies have attempted to measure this velocity bias and its effect by comparing the velocity divergence power spectrum measured from simulated halos and the corresponding dark matter field (de la Torre & Guzzo 2012; Elia et al 2012;Jennings et al 2015;Zheng et al 2015), however the magnitude of this effect remains largely uncertain due to numerical resolution issues and difficulties in measuring the velocity divergence power spectrum. Regardless, most studies agree that the effect of the velocity bias is small for k < 0.1 h Mpc −1 , and hence one could argue that it remains unimportant for current measurements of the growth rate of structure from peculiar velocities, which relies primarily on the information from linear scales.…”

Section: γ Parameterisationmentioning

confidence: 99%

Cosmological forecasts for combined and next-generation peculiar velocity surveys

Howlett

Staveley‐Smith

Blake

2016

Mon. Not. R. Astron. Soc.

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Peculiar velocity surveys present a very promising route to measuring the growth rate of largescale structure and its scale dependence. However, individual peculiar velocity surveys suffer from large statistical errors due to the intrinsic scatter in the relations used to infer a galaxy's true distance. In this context we use a Fisher Matrix formalism to investigate the statistical benefits of combining multiple peculiar velocity surveys. We find that for all cases we consider there is a marked improvement on constraints on the linear growth rate f σ 8 . For example, the constraining power of only a few peculiar velocity measurements is such that the addition of the 2MASS Tully-Fisher survey (containing only ∼ 2, 000 galaxies) to the full redshift and peculiar velocity samples of the 6-degree Field Galaxy Survey (containing ∼ 110, 000 redshifts and ∼ 9, 000 velocities) can improve growth rate constraints by ∼ 20%. Furthermore, the combination of the future TAIPAN and WALLABY+WNSHS surveys has the potential to reach a ∼ 3% error on f σ 8 , which will place tight limits on possible extensions to General Relativity. We then turn to look at potential systematics in growth rate measurements that can arise due to incorrect calibration of the peculiar velocity zero-point and from scale-dependent spatial and velocity bias. For next generation surveys, we find that neglecting velocity bias in particular has the potential to bias constraints on the growth rate by over 5σ, but that an offset in the zero-point has negligible impact on the velocity power spectrum.

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“…It consists of two parts, halo velocity bias b v and halo density bias b h . Prior to this study, the detailed investigation on halo velocity bias has been made [104,105]. In [105], authors provided an accurate fitting formula of b v at k 0.25 h Mpc −1 , valid for various halo mass bins at different redshifts.…”

Section: Introductionmentioning

confidence: 99%

Study on the mapping of halo clustering from real space to redshift space

Zheng

Song

2019

J. Cosmol. Astropart. Phys.

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The mapping of galaxy clustering from real space to redshift space introduces the anisotropic property to the measured galaxy density power spectrum in redshift space, known as the redshift space distortion (RSD) effect. The mapping formula is intrinsically non-linear, which is complicated by the higher order polynomials due to indefinite orders of cross correlations between density and velocity fields, and the Finger-of-God (FoG) effect due to the randomness of the galaxy peculiar velocity field. In previous works, we have verified the robustness of advanced TNS mapping formula in our hybrid RSD model in dark matter case, where the halo bias models are not taken into account for the halo mapping formula in redshift space. Using 100 realizations of halo catalogs in N-body simulations, we find that our halo RSD model with the known halo bias model and the effective FoG function accurately predicts the halo power spectrum measurements, within 1∼2% accuracy up to k ∼ 0.2 h Mpc −1 , depending on different halo masses and redshifts. Theoretical RSD model for halo clusteringUnderstanding the halo clustering in redshift space is a key stepstone towards theoretically describing the observed galaxy clustering in the Universe. In our previous work [96], the RSD model for dark matter clustering in redshift space was studied in detail. The model has been proved to accurately reconstruct the linear growth rate within 1% at k < 0.18 h Mpc −1 for simulations of different cosmologies with different Hubble parameters [97]. This theoretical RSD model will be applied to the halo clustering case in this manuscript. We will describe the RSD model in this section. Besides, the halo density bias model [108] and halo velocity bias model [105] adopted in this paper will be presented as well. The advanced TNS model for halosMatter distribution in the universe is inhomogeneous at small scales. The gravitational attraction arising from this inhomogeneity perturbs galaxies and causes their motions deviating from the Hubble flow. These deviations, named peculiar velocities of galaxies, disturb the galaxy redshifts and hence the galaxy distribution in redshift space in an anisotropic way.

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Determination of the large scale volume weighted halo velocity bias in simulations

Cited by 30 publications

References 24 publications

Sampling artifact in volume weighted velocity measurement. II. Detection in simulations and comparison with theoretical modeling

Sampling artifact in volume weighted velocity measurement. II. Detection in simulations and comparison with theoretical modeling

Cosmological forecasts for combined and next-generation peculiar velocity surveys

Study on the mapping of halo clustering from real space to redshift space

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