Joint analysis of anisotropic power spectrum, bispectrum and trispectrum: application to N-body simulations

Gualdi, Davide; Gil-Marín, Héctor; Verde, Licia

doi:10.1088/1475-7516/2021/07/008

Cited by 50 publications

(48 citation statements)

References 150 publications

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“…However, modeling the bispectrum is difficult. Although a wealth of previous studies have discussed its theoretical form, both for matter and galaxies [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46], it is only recently that we have obtained a theory model that is capable of predicting the full galaxy bispectrum shape in redshift-space to the precision required for current and future surveys [47]. Application of bispectrum models to true data is hampered by the effects of survey geometry.…”

Section: Introductionmentioning

confidence: 99%

The BOSS DR12 Full-Shape Cosmology: $Λ$CDM Constraints from the Large-Scale Galaxy Power Spectrum and Bispectrum Monopole

Philcox,

Ivanov

2021

Preprint

122

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We present a full ΛCDM analysis of the BOSS DR12 dataset, including information from the power spectrum multipoles, the real-space power spectrum, the reconstructed power spectrum and the bispectrum monopole. This is the first analysis to feature a complete treatment of the galaxy bispectrum, including a consistent theoretical model and without large-scale cuts. Unlike previous works, the statistics are measured using window-free estimators: this greatly reduces computational costs by removing the need to window-convolve the theory model. Our pipeline is tested using a suite of high-resolution mocks and shown to be robust and precise, with systematic errors far below the statistical thresholds. Inclusion of the bispectrum yields consistent parameter constraints and shrinks the σ8 posterior by 13% to reach < 5% precision; less conservative analysis choices would reduce the error-bars further. Our constraints are broadly consistent with Planck : in particular, we find H0 = 69.6 +1.1 −1.3 km s −1 Mpc −1 , σ8 = 0.692 +0.035 −0.041 and ns = 0.870 +0.067 −0.064 , including a BBN prior on the baryon density. When ns is set by Planck, we find H0 = 68.31 +0.83 −0.86 km s −1 Mpc −1 and σ8 = 0.722 +0.032 −0.036 . Our S8 posterior, 0.751 ± 0.039, is consistent with weak lensing studies, but lower than Planck. Constraints on the higher-order bias parameters are significantly strengthened from the inclusion of the bispectrum, and we find no evidence for deviation from the dark matter halo bias relations. These results represent the most complete full-shape analysis of BOSS DR12 to-date, and the corresponding spectra will enable a variety of beyond-ΛCDM analyses, probing phenomena such as the neutrino mass and primordial non-Gaussianity.

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Section: Introductionmentioning

confidence: 99%

The BOSS DR12 Full-Shape Cosmology: $Λ$CDM Constraints from the Large-Scale Galaxy Power Spectrum and Bispectrum Monopole

Philcox,

Ivanov

2021

Preprint

122

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“…Cole et al 2005;Tinker et al 2012;Alam et al 2017;Gil-Marín et al 2017). Numerous works also show that the first higher-order term, namely the bispectrum, carries nonnegligible information able to improve the constraints (Sefusatti et al 2006;Yankelevich & Porciani 2019;Hahn et al 2020;Hahn & Villaescusa-Navarro 2021;Agarwal et al 2021;Gualdi et al 2021). However, because of the difficulties of directly measuring and computing higher-order statistics (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Cosmology with cosmic web environments I. Real-space power spectra

Bonnaire,

Aghanim,

Kuruvilla

et al. 2021

Preprint

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We undertake the first comprehensive and quantitative real-space analysis of the cosmological information content in the environments of the cosmic web (voids, filaments, walls, and nodes) up to non-linear scales, k = 0.5 h/Mpc. Relying on the large set of N -body simulations from the Quijote suite, the environments are defined through the eigenvalues of the tidal tensor and the Fisher formalism is used to assess the constraining power of the power spectra derived in each of the four environments and their combination. Our results show that there is more information available in the environment-dependent power spectra, both individually and when combined all together, than in the matter power spectrum. By breaking some key degeneracies between parameters of the cosmological model such as Mν -σ8 or Ωm-σ8, the power spectra computed in identified environments improve the constraints on cosmological parameters by factors ∼ 15 for the summed neutrino mass Mν and ∼ 8 for the matter density Ωm over those derived from the matter power spectrum. We show that these tighter constraints are obtained for a wide range of the maximum scale, from kmax = 0.1 h/Mpc to highly non-linear regimes with kmax = 0.5 h/Mpc. We also report an eight times higher value of the signal-to-noise ratio for the combination of spectra compared to the matter one. Importantly, we show that all the presented results are robust to variations of the parameters defining the environments hence suggesting a robustness to the definition we chose to define them.

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“…The choice of statistic used to extract information from the survey data. Specifically, if 3-point correlations [12] or other statistics that go beyond 2-point correlations are used, especially in combination, then the parameter combinations probed can vary significantly [13][14][15][16][17]. As an example, 2-point correlations of the weak lensing shear field are widely characterized by two leading parameters, S 8 ≡ σ 8 Ω 0.5 m and Ω m .…”

Section: Introductionmentioning

confidence: 99%

What does a cosmological experiment really measure? Covariant posterior decomposition with normalizing flows

Dacunha,

Raveri,

Park

et al. 2021

Preprint

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We present methods to rigorously extract parameter combinations that are constrained by data from posterior distributions. The standard approach uses linear methods that apply to Gaussian distributions. We show the limitations of the linear methods for current surveys, and develop non-linear methods that can be used with non-Gaussian distributions, and are independent of the parameter basis. These are made possible by the use of machine-learning models, normalizing flows, to learn posterior distributions from their samples. These models allow us to obtain the local covariance of the posterior at all positions in parameter space and use its inverse, the Fisher matrix, as a local metric over parameter space. The posterior distribution can then be non-linearly decomposed into the leading constrained parameter combinations via parallel transport in the metric space. We test our methods on two non-Gaussian, benchmark examples, and then apply them to the parameter posteriors of the Dark Energy Survey and Planck CMB lensing. We illustrate how our method automatically learns the survey-specific, best constrained effective amplitude parameter S8 for cosmic shear alone, cosmic shear and galaxy clustering, and CMB lensing. We also identify constrained parameter combinations in the full parameter space, and as an application we estimate the Hubble constant, H0, from large-structure data alone.

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Joint analysis of anisotropic power spectrum, bispectrum and trispectrum: application to N-body simulations

Cited by 50 publications

References 150 publications

The BOSS DR12 Full-Shape Cosmology: $Λ$CDM Constraints from the Large-Scale Galaxy Power Spectrum and Bispectrum Monopole

The BOSS DR12 Full-Shape Cosmology: $Λ$CDM Constraints from the Large-Scale Galaxy Power Spectrum and Bispectrum Monopole

Cosmology with cosmic web environments I. Real-space power spectra

What does a cosmological experiment really measure? Covariant posterior decomposition with normalizing flows

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