scholarly journals Maximum entropy deconvolution of primordial power spectrum

2013 ◽  
Vol 88 (2) ◽  
Author(s):  
Gaurav Goswami ◽  
Jayanti Prasad
Keyword(s):  
Power Spectrum ◽  
Maximum Entropy ◽  
Primordial Power Spectrum ◽  
Maximum Entropy Deconvolution

SIMS Profile quantification by maximum entropy deconvolution

1993 ◽  
Vol 20 (8) ◽  
pp. 696-702 ◽  
Author(s):  
P. N. Allen ◽  
M. G. Dowsett ◽  
R. Collins
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Deconvolution

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 Mammographic image restoration using maximum entropy deconvolution

2004 ◽  
Vol 49 (21) ◽  
pp. 4997-5010 ◽  
Author(s):  
A Jannetta ◽  
J C Jackson ◽  
C J Kotre ◽  
I P Birch ◽  
K J Robson ◽  
...  
Keyword(s):  
Image Restoration ◽  
Maximum Entropy ◽  
Mammographic Image ◽  
Maximum Entropy Deconvolution

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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