scholarly journals Cosmological parameter estimation with free-form primordial power spectrum

2013 ◽  
Vol 87 (12) ◽  
Author(s):  
Dhiraj Kumar Hazra ◽  
Arman Shafieloo ◽  
Tarun Souradeep
Keyword(s):  
Parameter Estimation ◽  
Power Spectrum ◽  
Cosmological Parameter ◽  
Free Form ◽  
Primordial Power Spectrum

scholarly journals Cosmological Parameter Estimation from the Two-dimensional Genus Topology: Measuring the Shape of the Matter Power Spectrum

2020 ◽  
Vol 896 (2) ◽  
pp. 145
Author(s):  
Stephen Appleby ◽  
Changbom Park ◽  
Sungwook E. Hong ◽  
Ho Seong Hwang ◽  
Juhan Kim
Keyword(s):  
Parameter Estimation ◽  
Power Spectrum ◽  
Cosmological Parameter ◽  
Two Dimensional ◽  
Matter Power Spectrum

scholarly journals Reconstruction of the primordial power spectrum by direct inversion

2010 ◽  
Vol 2010 (01) ◽  
pp. 016-016 ◽  
Author(s):  
Gavin Nicholson ◽  
Carlo R Contaldi ◽  
Paniez Paykari
Keyword(s):  
Power Spectrum ◽  
Direct Inversion ◽  
Primordial Power Spectrum

scholarly journals Simultaneously constraining the astrophysics of reionisation and the epoch of heating with 21CMMC

2017 ◽  
Vol 12 (S333) ◽  
pp. 18-21 ◽  
Author(s):  
Bradley Greig ◽  
Andrei Mesinger
Keyword(s):  
Parameter Estimation ◽  
Theoretical Analysis ◽  
Power Spectrum ◽  
Light Cone ◽  
Theoretical Modelling ◽  
Next Generation ◽  
One Step ◽  
Foreground Removal ◽  
Spectrum Amplitude

AbstractWe extend our MCMC sampler of 3D EoR simulations, 21CMMC, to perform parameter estimation directly on light-cones of the cosmic 21cm signal. This brings theoretical analysis one step closer to matching the expected 21-cm signal from next generation interferometers like HERA and the SKA. Using the light-cone version of 21CMMC, we quantify biases in the recovered astrophysical parameters obtained from the 21cm power spectrum when using the co-eval approximation to fit a mock 3D light-cone observation. While ignoring the light-cone effect does not bias the parameters under most assumptions, it can still underestimate their uncertainties. However, significant biases (∼few – 10 σ) are possible if all of the following conditions are met: (i) foreground removal is very efficient, allowing large physical scales (k ∼ 0.1 Mpc−1) to be used in the analysis; (ii) theoretical modelling is accurate to ∼10 per cent in the power spectrum amplitude; and (iii) the 21cm signal evolves rapidly (i.e. the epochs of reionisation and heating overlap significantly

scholarly journals Constraints on primordial power spectrum from galaxy luminosity functions

2020 ◽  
Vol 102 (8) ◽  
Author(s):  
Shintaro Yoshiura ◽  
Masamune Oguri ◽  
Keitaro Takahashi ◽  
Tomo Takahashi
Keyword(s):  
Power Spectrum ◽  
Primordial Power Spectrum ◽  
Luminosity Functions

scholarly journals Primordial power spectrum and cosmology from black-box galaxy surveys

2019 ◽  
Vol 490 (3) ◽  
pp. 4237-4253 ◽  
Author(s):  
Florent Leclercq ◽  
Wolfgang Enzi ◽  
Jens Jasche ◽  
Alan Heavens
Keyword(s):  
Power Spectrum ◽  
Numerical Models ◽  
A Priori ◽  
Cosmological Parameters ◽  
Physical Structure ◽  
Black Box ◽  
Acoustic Oscillations ◽  
Galaxy Surveys ◽  
Matter Power Spectrum ◽  
Primordial Power Spectrum

ABSTRACT We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i.e. black boxes. Our approach, which we call simulator expansion for likelihood-free inference (selfi), builds upon approximate Bayesian computation using a novel effective likelihood, and upon the linearization of black-box models around an expansion point. Consequently, we obtain simple ‘filter equations’ for an effective posterior of the primordial power spectrum, and a straightforward scheme for cosmological parameter inference. We demonstrate that the workload is computationally tractable, fixed a priori, and perfectly parallel. As a proof of concept, we apply our framework to a realistic synthetic galaxy survey, with a data model accounting for physical structure formation and incomplete and noisy galaxy observations. In doing so, we show that the use of non-linear numerical models allows the galaxy power spectrum to be safely fitted up to at least kmax = 0.5 h Mpc−1, outperforming state-of-the-art backward-modelling techniques by a factor of ∼5 in the number of modes used. The result is an unbiased inference of the primordial matter power spectrum across the entire range of scales considered, including a high-fidelity reconstruction of baryon acoustic oscillations. It translates into an unbiased and robust inference of cosmological parameters. Our results pave the path towards easy applications of likelihood-free simulation-based inference in cosmology. We have made our code pyselfi and our data products publicly available at http://pyselfi.florent-leclercq.eu.

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