Super-sample covariance of the power spectrum, bispectrum, halos, voids, and their cross covariances

Bayer, Adrian E.; Liu, Jia; Terasawa, Ryo; Zhong, Yici; Feng, Yu

doi:10.1103/physrevd.108.043521

“…Third, we have neglected the effect of super-sample covariance in our analysis due to the finite volume of the QUIJOTE simulations. However, a recent study has shown that the effect of supersample covariance on the void size function is small (Bayer et al 2023).…”

Section: Discussionmentioning

confidence: 99%

Machine-learning Cosmology from Void Properties

Wang 汪,

Pisani,

Villaescusa-Navarro

et al. 2023

ApJ

5

0

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Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine-learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES data set, where every catalog contains an average of 11,000 voids from a volume of 1 h − 1 Gpc 3 . We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train (1) fully connected neural networks on histograms from individual void properties and (2) deep sets from void catalogs to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of Ω m , σ 8, and n s with mean relative errors of 10%, 4%, and 3%, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.

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“…The side length of the GLAM simulation box is 1 h −1 Gpc. Simulations with a larger box size can help investigate the effect of supersample covariance (SSC) (Bayer et al 2023). Limited by the simulation suite, the covariance determination in this work neglects supersample variance components.…”

Section: Covariance Matrixmentioning

confidence: 99%

Emulating Power Spectra for Prereconstructed and Postreconstructed Galaxy Samples

Wang,

Zhao,

Zhai

et al. 2024

ApJ

0

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The small-scale linear information in galaxy samples typically lost during nonlinear growth can be restored to a certain level by the density field reconstruction, which has been demonstrated for improving the precision of the baryon acoustic oscillation (BAO) measurements. As proposed in the literature, a joint analysis of the power spectrum before and after the reconstruction enables an efficient extraction of information carried by high-order statistics. However, the statistics of the postreconstruction density field are difficult to model. In this work, we circumvent this issue by developing an accurate emulator for the prereconstructed, postreconstructed, and cross-power spectra ( P pre , P post, P cross) up to k = 0.5 h Mpc−1 based on the Dark Quest N-body simulations. The accuracy of the emulator is at the percent level; namely, the error of the emulated monopole and quadrupole of the power spectra is less than 1% and 10% of the ground truth, respectively. A fit to an example power spectrum using the emulator shows that the constraints on cosmological parameters get largely improved using P pre +P post+P cross with k max = 0.25 h Mpc − 1 , compared to that derived from P pre alone; namely, the constraints on (Ω m , H 0, σ 8) are tightened by ∼41%–55%, and the uncertainties of the derived BAO and RSD parameters (α ⊥, α ∣∣, f σ 8) shrink by ∼28%–54%, respectively. This highlights the complementarity among P pre , P post, and P cross, which demonstrates the efficiency and practicability of a joint P pre , P post, and P cross analysis for cosmological implications.

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“…These types of finite-volume effects are characterized in more detail in section 3.3 of ref. [99], and can be understood more broadly as a combination of a super-sample mode effect [100][101][102][103][104], as the small-box simulation is effectively missing large-scale power that is present in the larger simulation box (see e.g. figure 6 of ref.…”

Section: Emulator Comparisonmentioning

confidence: 99%

Improving initialization and evolution accuracy of cosmological neutrino simulations

Sullivan

¹

,

Emberson

²

,

Habib

³

et al. 2023

J. Cosmol. Astropart. Phys.

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Neutrino mass constraints are a primary focus of current and future large-scale structure (LSS) surveys. Non-linear LSS models rely heavily on cosmological simulations — the impact of massive neutrinos should therefore be included in these simulations in a realistic, computationally tractable, and controlled manner. A recent proposal to reduce the related computational cost employs a symmetric neutrino momentum sampling strategy in the initial conditions. We implement a modified version of this strategy into the Hardware/Hybrid Accelerated Cosmology Code (HACC) and perform convergence tests on its internal parameters. We illustrate that this method can impart 𝒪(1%) numerical artifacts on the total matter field on small scales, similar to previous findings, and present a method to remove these artifacts using Fourier-space filtering of the neutrino density field. Moreover, we show that the converged neutrino power spectrum does not follow linear theory predictions on relatively large scales at early times at the 15% level, prompting a more careful study of systematics in particle-based neutrino simulations. We also present an improved method for backscaling linear transfer functions for initial conditions in massive neutrino cosmologies that is based on achieving the same relative neutrino growth as computed with Boltzmann solvers. Our self-consistent backscaling method yields sub-percent accuracy in the total matter growth function. Comparisons for the non-linear power spectrum with the Mira-Titan emulator at a neutrino mass of mν = 0.15 eV are in very good agreement with the expected level of errors in the emulator and in the direct N-body simulation.

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Super-sample covariance of the power spectrum, bispectrum, halos, voids, and their cross covariances

Cited by 7 publications

References 98 publications

Machine-learning Cosmology from Void Properties

Machine-learning Cosmology from Void Properties

Emulating Power Spectra for Prereconstructed and Postreconstructed Galaxy Samples

Improving initialization and evolution accuracy of cosmological neutrino simulations

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