scholarly journals Bayesian forward modelling of cosmic shear data

2021 ◽  
Vol 502 (2) ◽  
pp. 3035-3044
Author(s):  
Natalia Porqueres ◽  
Alan Heavens ◽  
Daniel Mortlock ◽  
Guilhem Lavaux
Keyword(s):  
Power Spectrum ◽  
Power Spectra ◽  
Density Field ◽  
Matter Distribution ◽  
Bayesian Hierarchical ◽  
Matter Power Spectrum ◽  
Field Samples ◽  
Dark Matter Distribution ◽  
Cosmic Shear ◽  
Non Gaussian

ABSTRACT We present a Bayesian hierarchical modelling approach to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data. This method uses a physical model of cosmic structure formation to infer physically plausible cosmic structures, which accounts for the non-Gaussian features of the gravitationally evolved matter distribution and light-cone effects. We test and validate our framework with realistic simulated shear data, demonstrating that the method recovers the unbiased matter distribution and the correct lensing and matter power spectrum. While the cosmology is fixed in this test, and the method employs a prior power spectrum, we demonstrate that the lensing results are sensitive to the true power spectrum when this differs from the prior. In this case, the density field samples are generated with a power spectrum that deviates from the prior, and the method recovers the true lensing power spectrum. The method also recovers the matter power spectrum across the sky, but as currently implemented, it cannot determine the radial power since isotropy is not imposed. In summary, our method provides physically plausible inference of the dark matter distribution from cosmic shear data, allowing us to extract information beyond the two-point statistics and exploiting the full information content of the cosmological fields.

scholarly journals Cosmological model parameter dependence of the matter power spectrum covariance from the DEUS-PUR Cosmo simulations

2020 ◽  
Vol 500 (2) ◽  
pp. 2532-2542
Author(s):  
Linda Blot ◽  
Pier-Stefano Corasaniti ◽  
Yann Rasera ◽  
Shankar Agarwal
Keyword(s):  
Dark Energy ◽  
Power Spectrum ◽  
Cold Dark Matter ◽  
Matter Density ◽  
Density Fluctuations ◽  
Matrix Analysis ◽  
Model Parameter ◽  
Matter Power Spectrum ◽  
Non Gaussian ◽  
The Impact

ABSTRACT Future galaxy surveys will provide accurate measurements of the matter power spectrum across an unprecedented range of scales and redshifts. The analysis of these data will require one to accurately model the imprint of non-linearities of the matter density field. In particular, these induce a non-Gaussian contribution to the data covariance that needs to be properly taken into account to realize unbiased cosmological parameter inference analyses. Here, we study the cosmological dependence of the matter power spectrum covariance using a dedicated suite of N-body simulations, the Dark Energy Universe Simulation–Parallel Universe Runs (DEUS-PUR) Cosmo. These consist of 512 realizations for 10 different cosmologies where we vary the matter density Ωm, the amplitude of density fluctuations σ8, the reduced Hubble parameter h, and a constant dark energy equation of state w by approximately $10{{\ \rm per\ cent}}$. We use these data to evaluate the first and second derivatives of the power spectrum covariance with respect to a fiducial Λ-cold dark matter cosmology. We find that the variations can be as large as $150{{\ \rm per\ cent}}$ depending on the scale, redshift, and model parameter considered. By performing a Fisher matrix analysis we explore the impact of different choices in modelling the cosmological dependence of the covariance. Our results suggest that fixing the covariance to a fiducial cosmology can significantly affect the recovered parameter errors and that modelling the cosmological dependence of the variance while keeping the correlation coefficient fixed can alleviate the impact of this effect.

scholarly journals Sufficiency of a Gaussian power spectrum likelihood for accurate cosmology from upcoming weak lensing surveys

2021 ◽  
Author(s):  
Robin E Upham ◽  
Michael L Brown ◽  
Lee Whittaker
Keyword(s):  
Power Spectrum ◽  
Random Noise ◽  
Power Spectra ◽  
Stage Iv ◽  
Wishart Distribution ◽  
Weak Lensing ◽  
Dependence Structure ◽  
Gaussian Fields ◽  
Non Gaussian ◽  
Parameter Constraints

Abstract We investigate whether a Gaussian likelihood is sufficient to obtain accurate parameter constraints from a Euclid-like combined tomographic power spectrum analysis of weak lensing, galaxy clustering and their cross-correlation. Testing its performance on the full sky against the Wishart distribution, which is the exact likelihood under the assumption of Gaussian fields, we find that the Gaussian likelihood returns accurate parameter constraints. This accuracy is robust to the choices made in the likelihood analysis, including the choice of fiducial cosmology, the range of scales included, and the random noise level. We extend our results to the cut sky by evaluating the additional non-Gaussianity of the joint cut-sky likelihood in both its marginal distributions and dependence structure. We find that the cut-sky likelihood is more non-Gaussian than the full-sky likelihood, but at a level insufficient to introduce significant inaccuracy into parameter constraints obtained using the Gaussian likelihood. Our results should not be affected by the assumption of Gaussian fields, as this approximation only becomes inaccurate on small scales, which in turn corresponds to the limit in which any non-Gaussianity of the likelihood becomes negligible. We nevertheless compare against N-body weak lensing simulations and find no evidence of significant additional non-Gaussianity in the likelihood. Our results indicate that a Gaussian likelihood will be sufficient for robust parameter constraints with power spectra from Stage IV weak lensing surveys.

scholarly journals Emulation of baryonic effects on the matter power spectrum and constraints from galaxy cluster data

2021 ◽  
Vol 2021 (12) ◽  
pp. 046
Author(s):  
Sambit K. Giri ◽  
Aurel Schneider
Keyword(s):  
Power Spectrum ◽  
Percent Level ◽  
Power Spectra ◽  
Galaxy Cluster ◽  
Parameter Study ◽  
Galaxy Groups ◽  
Matter Power Spectrum ◽  
Cluster Data ◽  
Percent Confidence Level ◽  
Dynamical Simulations

Abstract Baryonic feedback effects consist of a major systematic for upcoming weak-lensing and galaxy-clustering surveys. In this paper, we present an emulator for the baryonic suppression of the matter power spectrum. The emulator is based on the baryonification model, containing seven free parameters that are connected to the gas profiles and stellar abundances in haloes. We show that with the baryonic emulator, we can not only recover the power spectra of hydro-dynamical simulations at sub-percent precision, but also establish a connection between the baryonic suppression of the power spectrum and the gas and stellar fractions in haloes. This connection allows us to predict the expected deviation from a dark-matter-only power spectrum using measured X-ray gas fractions of galaxy groups and clusters. With these measurements, we constrain the suppression to exceed the percent-level at k=0.1-0.4 h/Mpc and to reach a maximum of 20-28 percent at around k∼ 7 h/Mpc (68 percent confidence level). As a further step, we also perform a detailed parameter study and we present a minimum set of four baryonic parameters that are required to recover the scale and redshift dependence observed in hydro-dynamical simulations. The baryonic emulator can be found at https://github.com/sambit-giri/BCemu.

scholarly journals NON-GAUSSIAN ERROR CONTRIBUTION TO LIKELIHOOD ANALYSIS OF THE MATTER POWER SPECTRUM

2010 ◽  
Vol 726 (1) ◽  
pp. 7 ◽  
Author(s):  
Ryuichi Takahashi ◽  
Naoki Yoshida ◽  
Masahiro Takada ◽  
Takahiko Matsubara ◽  
Naoshi Sugiyama ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Matter Power Spectrum ◽  
Likelihood Analysis ◽  
Non Gaussian

scholarly journals On the road to per cent accuracy – II. Calibration of the non-linear matter power spectrum for arbitrary cosmologies

2019 ◽  
Vol 490 (4) ◽  
pp. 4826-4840 ◽  
Author(s):  
Benjamin Giblin ◽  
Matteo Cataneo ◽  
Ben Moews ◽  
Catherine Heymans
Keyword(s):  
Power Spectrum ◽  
Initial Conditions ◽  
Power Spectra ◽  
Linear Regime ◽  
The Road ◽  
Matter Power Spectrum ◽  
Non Linear ◽  
Standard Cosmological Model ◽  
Massive Neutrinos ◽  
On The Road

ABSTRACT We introduce an emulator approach to predict the non-linear matter power spectrum for broad classes of beyond-ΛCDM cosmologies, using only a suite of ΛCDM N-body simulations. By including a range of suitably modified initial conditions in the simulations, and rescaling the resulting emulator predictions with analytical ‘halo model reactions’, accurate non-linear matter power spectra for general extensions to the standard ΛCDM model can be calculated. We optimize the emulator design by substituting the simulation suite with non-linear predictions from the standard halofit tool. We review the performance of the emulator for artificially generated departures from the standard cosmology as well as for theoretically motivated models, such as f(R) gravity and massive neutrinos. For the majority of cosmologies we have tested, the emulator can reproduce the matter power spectrum with errors ${\lesssim}1{{\ \rm per\ cent}}$ deep into the highly non-linear regime. This work demonstrates that with a well-designed suite of ΛCDM simulations, extensions to the standard cosmological model can be tested in the non-linear regime without any reliance on expensive beyond-ΛCDM simulations.

scholarly journals Matter power spectrum: from Ly α forest to CMB scales

2019 ◽  
Vol 489 (2) ◽  
pp. 2247-2253 ◽  
Author(s):  
Solène Chabanier ◽  
Marius Millea ◽  
Nathalie Palanque-Delabrouille
Keyword(s):  
Power Spectrum ◽  
Power Spectra ◽  
Model Fit ◽  
Microwave Background ◽  
Luminous Red Galaxies ◽  
Matter Power Spectrum ◽  
Physical Scale ◽  
Point Correlation ◽  
Point Correlation Function ◽  
Red Galaxies

ABSTRACT We present a new compilation of inferences of the linear 3D matter power spectrum at redshift $z\, {=}\, 0$ from a variety of probes spanning several orders of magnitude in physical scale and in cosmic history. We develop a new lower noise method for performing this inference from the latest Ly α forest 1D power spectrum data. We also include cosmic microwave background (CMB) temperature and polarization power spectra and lensing reconstruction data, the cosmic shear two-point correlation function, and the clustering of luminous red galaxies. We provide a Dockerized Jupyter notebook housing the fairly complex dependences for producing the plot of these data, with the hope that groups in the future can help add to it. Overall, we find qualitative agreement between the independent measurements considered here and the standard ΛCDM cosmological model fit to the Planck data.

scholarly journals Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data

2019 ◽  
Vol 71 (2) ◽  
Author(s):  
Chiaki Hikage ◽  
Masamune Oguri ◽  
Takashi Hamana ◽  
Surhud More ◽  
Rachel Mandelbaum ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Cold Dark Matter ◽  
Signal To Noise Ratio ◽  
Power Spectra ◽  
First Year ◽  
Ratio Test ◽  
Photometric Redshifts ◽  
Spectrum Measurement ◽  
Bayesian Evidence ◽  
Cosmic Shear

Abstract We measure cosmic weak lensing shear power spectra with the Subaru Hyper Suprime-Cam (HSC) survey first-year shear catalog covering 137 deg2 of the sky. Thanks to the high effective galaxy number density of ∼17 arcmin−2, even after conservative cuts such as a magnitude cut of i < 24.5 and photometric redshift cut of 0.3 ≤ z ≤ 1.5, we obtain a high-significance measurement of the cosmic shear power spectra in four tomographic redshift bins, achieving a total signal-to-noise ratio of 16 in the multipole range 300 ≤ ℓ ≤ 1900. We carefully account for various uncertainties in our analysis including the intrinsic alignment of galaxies, scatters and biases in photometric redshifts, residual uncertainties in the shear measurement, and modeling of the matter power spectrum. The accuracy of our power spectrum measurement method as well as our analytic model of the covariance matrix are tested against realistic mock shear catalogs. For a flat Λ cold dark matter model, we find $S\,_{8}\equiv \sigma _8(\Omega _{\rm m}/0.3)^\alpha =0.800^{+0.029}_{-0.028}$ for α = 0.45 ($S\,_8=0.780^{+0.030}_{-0.033}$ for α = 0.5) from our HSC tomographic cosmic shear analysis alone. In comparison with Planck cosmic microwave background constraints, our results prefer slightly lower values of S8, although metrics such as the Bayesian evidence ratio test do not show significant evidence for discordance between these results. We study the effect of possible additional systematic errors that are unaccounted for in our fiducial cosmic shear analysis, and find that they can shift the best-fit values of S8 by up to ∼0.6 σ in both directions. The full HSC survey data will contain several times more area, and will lead to significantly improved cosmological constraints.

scholarly journals Power spectrum of halo intrinsic alignments in simulations

2020 ◽  
Vol 501 (1) ◽  
pp. 833-852
Author(s):  
Toshiki Kurita ◽  
Masahiro Takada ◽  
Takahiro Nishimichi ◽  
Ryuichi Takahashi ◽  
Ken Osato ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Cosmological Parameters ◽  
Three Dimensional ◽  
Power Spectra ◽  
Acoustic Oscillations ◽  
Matter Power Spectrum ◽  
Sample Covariance ◽  
Non Linear

ABSTRACT We use a suite of N-body simulations to study intrinsic alignments (IA) of halo shapes with the surrounding large-scale structure in the ΛCDM model. For this purpose, we develop a novel method to measure multipole moments of the three-dimensional power spectrum of the E-mode field of halo shapes with the matter/halo distribution, $P_{\delta E}^{(\ell)}(k)$ (or $P^{(\ell)}_{{\rm h}E}$), and those of the auto-power spectrum of the E-mode, $P^{(\ell)}_{EE}(k)$, based on the E/B-mode decomposition. The IA power spectra have non-vanishing amplitudes over the linear to non-linear scales, and the large-scale amplitudes at k ≲ 0.1 h−1 Mpc are related to the matter power spectrum via a constant coefficient (AIA), similar to the linear bias parameter of galaxy or halo density field. We find that the cross- and auto-power spectra PδE and PEE at non-linear scales, k ≳ 0.1 h−1 Mpc, show different k-dependences relative to the matter power spectrum, suggesting a violation of the non-linear alignment model commonly used to model contaminations of cosmic shear signals. The IA power spectra exhibit baryon acoustic oscillations, and vary with halo samples of different masses, redshifts, and cosmological parameters (Ωm, S8). The cumulative signal-to-noise ratio for the IA power spectra is about 60 per cent of that for the halo density power spectrum, where the super-sample covariance is found to give a significant contribution to the total covariance. Thus our results demonstrate that the IA power spectra of galaxy shapes, measured from imaging and spectroscopic surveys for an overlapping area of the sky, can be used to probe the underlying matter power spectrum, the primordial curvature perturbations, and cosmological parameters, in addition to the standard galaxy density power spectrum.

scholarly journals Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys

2019 ◽  
Vol 624 ◽  
pp. A115 ◽  
Author(s):  
Natalia Porqueres ◽  
Doogesh Kodi Ramanah ◽  
Jens Jasche ◽  
Guilhem Lavaux
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Large Scale ◽  
Scale Effects ◽  
Power Spectra ◽  
Matter Density ◽  
Data Set ◽  
Dark Matter Density ◽  
Matter Power Spectrum ◽  
Robust Likelihood

The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce erroneous large-scale effects in recovered power spectra and inferred dark matter density fields. In this work, we present an effective solution to this problem in the form of a robust likelihood designed to account for effects due to unknown foreground and target contaminations. Conceptually, this robust likelihood marginalizes over the unknown large-scale contamination amplitudes. We showcase the effectiveness of this novel likelihood via an application to a mock SDSS-III data set subject to dust extinction contamination. In order to illustrate the performance of our proposed likelihood, we infer the underlying dark-matter density field and reconstruct the matter power spectrum, being maximally agnostic about the foregrounds. The results are compared to those of an analysis with a standard Poissonian likelihood, as typically used in modern large-scale structure analyses. While the standard Poissonian analysis yields excessive power for large-scale modes and introduces an overall bias in the power spectrum, our likelihood provides unbiased estimates of the matter power spectrum over the entire range of Fourier modes considered in this work. Further, we demonstrate that our approach accurately accounts for and corrects the effects of unknown foreground contaminations when inferring three-dimensional density fields. Robust likelihood approaches, as presented in this work, will be crucial to control unknown systematic error and maximize the outcome of the decadal surveys.

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