Sampling artifact in volume weighted velocity measurement. I. Theoretical modeling

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

doi:10.1103/physrevd.91.043522

Cited by 30 publications

(32 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, since all randomly selected DM populations at the same redshift have statistically identical spatial clustering, the sampling artifact can be robustly isolated, as is done in [12]. The present paper extends its numerical verification and quantification to a much wider range of DM density and redshift, and it is better theory motivated and backed up [4]. By comparing the sampling artifact of DM populations with various number densities at various redshifts, we hope to develop generic theoretical modeling of the sampling artifact, applicable to a wide range of particle number densities and spatial clustering.…”

Section: Introductionmentioning

confidence: 70%

“…In Sec. III we compare our results with the theoretical modeling developed in paper I [4]. We also propose an ansatz to further improve its accuracy.…”

Section: Introductionmentioning

confidence: 75%

“…In [4] (hereafter paper I) we present a theoretical modeling of the sampling artifact in measuring the volume weighted velocity power spectrum. The present paper (paper II) focuses on its numerical verification in order to improve our understanding of the sampling artifact.…”

Section: Introductionmentioning

confidence: 99%

“…For example, by comparing the velocity power spectrum P v of two halo populations at equal volume density, the difference in P v is significantly suppressed [13], manifesting the existence of a sampling artifact. One complexity is that the sampling artifact also depends on the spatial clustering of halos/DM particles [4]. Hence two halo populations of identical number density but different mass do not have an identical sampling artifact, due to differences in their spatial clustering.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2015

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 70%

“…In Sec. III we compare our results with the theoretical modeling developed in paper I [4]. We also propose an ansatz to further improve its accuracy.…”

Section: Introductionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…This resolution allows us to avoid sampling artifacts in the velocity power spectrum up to k h 1 Mpc 1 -(see Figure 3 of Zhang et al 2015). We then adaptively smooth particles to a grid of 1008 3 ( ) cells.…”

Section: Simulationmentioning

confidence: 99%

The Impact of Nonlinear Structure Formation on the Power Spectrum of Transverse Momentum Fluctuations and the Kinetic Sunyaev–zel’dovich Effect

Park

Komatsu

Shapiro

et al. 2016

ApJ

View full text Add to dashboard Cite

Cosmological transverse momentum fields, whose directions are perpendicular to Fourier wave vectors, induce temperature anisotropies in the cosmic microwave background via the kinetic Sunyaev-Zel'dovich (kSZ) effect. The transverse momentum power spectrum contains the four-point function of density and velocity fields, vv . dd á ñ In the post-reionization epoch, nonlinear effects dominate in the power spectrum. We use perturbation theory and cosmological N-body simulations to calculate this nonlinearity. We derive the next-to-leading order expression for the power spectrum with a particular emphasis on the connected term that has been ignored in the literature. While the contribution from the connected term on small scales (k h 0.1 Mpc 1 > -) is subdominant relative to the unconnected term, we find that its contribution to the kSZ power spectrum at ℓ 3000 = at z 6 < can be as large as ten percent of the unconnected term, which would reduce the allowed contribution from the reionization epoch (z 6 > ) by twenty percent. The power spectrum of transverse momentum on large scales is expected to scale as k 2 as a consequence of momentum conservation. We show that both the leading and the next-to-leading order terms satisfy this scaling. In particular, we find that both of the unconnected and connected terms are necessary to reproduce k 2 .

show abstract

Large-scale dark matter simulations

Angulo

Hahn

2022

Living Rev Comput Astrophys

111

View full text Add to dashboard Cite

We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then recap the relevant numerical approaches: discretization of the phase-space distribution (focusing on N-body but including alternatives, e.g., Lagrangian submanifold and Schrödinger–Poisson) and the respective techniques for their time evolution and force calculation (direct summation, mesh techniques, and hierarchical tree methods). We pay attention to the creation of initial conditions and the connection with Lagrangian Perturbation Theory. We then discuss the possible alternatives in terms of the micro-physical properties of dark matter (e.g., neutralinos, warm dark matter, QCD axions, Bose–Einstein condensates, and primordial black holes), and extensions to account for multiple fluids (baryons and neutrinos), primordial non-Gaussianity and modified gravity. We continue by discussing challenges involved in achieving highly accurate predictions. A key aspect of cosmological simulations is the connection to cosmological observables, we discuss various techniques in this regard: structure finding, galaxy formation and baryonic modelling, the creation of emulators and light-cones, and the role of machine learning. We finalise with a recount of state-of-the-art large-scale simulations and conclude with an outlook for the next decade.

show abstract

Sampling artifact in volume weighted velocity measurement. I. Theoretical modeling

Cited by 30 publications

References 13 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

The Impact of Nonlinear Structure Formation on the Power Spectrum of Transverse Momentum Fluctuations and the Kinetic Sunyaev–zel’dovich Effect

Large-scale dark matter simulations

Contact Info

Product

Resources

About