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

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

doi:10.1103/physrevd.91.043523

Cited by 25 publications

(29 citation statements)

References 18 publications

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“…We separately construct 10 subsamples with f = 75%, 50%, and 25%,, and the measured η(k|f ) are shown in figure 4 We take η(k|f = 50%) as an indicator of the sampling artifact, showing that our velocity power spectrum measurement is accurate within 1.0% ∼ 1.5% at k 0.2h/Mpc. At higher redshifts, the velocity measurement tends to be more accurate [55].…”

Section: A2 the Velocity Field Sampling Artifactsmentioning

confidence: 99%

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Quantification of the multi-streaming effect in redshift space distortion

Zheng

Zhang

2017

J. Cosmol. Astropart. Phys.

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Abstract. Both multi-streaming (random motion) and bulk motion cause the Finger-ofGod (FoG) effect in redshift space distortion (RSD). We apply a direct measurement of the multi-streaming effect in RSD from simulations, proving that it induces an additional, non-negligible FoG damping to the redshift space density power spectrum. We show that, including the multi-streaming effect, the RSD modelling is significantly improved. We also provide a theoretical explanation based on halo model for the measured effect, including a fitting formula with one to two free parameters. The improved understanding of FoG helps break the f σ 8 − σ v degeneracy in RSD cosmology, and has the potential of significantly improving cosmological constraints.

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Section: A2 the Velocity Field Sampling Artifactsmentioning

confidence: 99%

“…We assign the more rigorous test to the future study. Also the sampling artifact could be partially corrected following the method proposed in [54,55,74].…”

Section: A2 the Velocity Field Sampling Artifactsmentioning

confidence: 99%

Quantification of the multi-streaming effect in redshift space distortion

Zheng

Zhang

2017

J. Cosmol. Astropart. Phys.

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“…b v (k, z), the halo velocity bias, describes how much the halo velocity field traces that of the underlying dark matter field. b v of realistic halos, after correcting the otherwise significant sampling artifact [114,115], was first measured in [104]. Recently, [105] developed a novel strategy to overcome the sampling artifact problem and determined this important bias parameter to 0.1 − 1% accuracy at k 0.4 h Mpc −1 and 0 < z < 2, for various halo mass bins.…”

Section: 3mentioning

confidence: 99%

“…(2.8) require the sampling of volume-weighted halo velocity field. However, halos are sparsely and inhomogeneously distributed, thus the sampled velocity field is severely contaminated by the sampling artifact even at the linear scales of k ∼ 0.1 h Mpc −1 [114,115]. As shown in eq.…”

Section: The Effect Of Halo Velocity Biasmentioning

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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“…For sparse samples such as massive halos, the problem is much more severe. For example, when n P ∼ 10 −4 Mpc −3 , the induced error in the velocity power spectrum increases to O(10)%, even at scales as large as k = 0.1h/Mpc [24].…”

Section: Introductionmentioning

confidence: 99%

Kriging interpolating cosmic velocity field. II. Taking anistropies and multistreaming into account

Zhang

Jing

et al. 2017

Phys. Rev. D

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Measuring the volume-weighted peculiar velocity statistics from inhomogeneously and sparsely distributed galaxies/halos, by existing velocity assignment methods, suffers from a significant sampling artifact. As an alternative, the Kriging interpolation based on Gaussian processes was introduced and evaluated [Y. Yu, J. Zhang, Y. Jing, and P. Zhang, Phys. Rev. D 92, 083527 (2015)]. Unfortunately, the most straightforward application of Kriging does not perform better than the existing methods in the literature. In this work, we investigate two physically motivated extensions. The first takes into account of the anisotropic velocity correlations. The second introduces the nugget effect, on account of multi-streaming of the velocity field. We find that the performance is indeed improved. For sparsely sampled data [nP 6 × 10 −3 (h −1 Mpc) −3 ] where the sampling artifact is the most severe, the improvement is significant and is two-fold: 1) The scale of reliable measurement of the velocity power spectrum is extended by a factor ∼ 1.6, and 2) the dependence on the velocity correlation prior is weakened by a factor of ∼ 2. We conclude that such extensions are desirable for accurate velocity assignment by Kriging. 95.36.+x, 98.80.Bp

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

Cited by 25 publications

References 18 publications

Quantification of the multi-streaming effect in redshift space distortion

Quantification of the multi-streaming effect in redshift space distortion

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

Kriging interpolating cosmic velocity field. II. Taking anistropies and multistreaming into account

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