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Title: Towards optimal extraction of cosmological information from nonlinear data

Abstract

One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics of clusters, density peaks or voids etc.), along with the corresponding covariance matrices. However, this approach is computationally prohibitive and has not been shown to be exhaustive in terms of information content. Here we instead develop a hierarchical Bayesian approach, expanding the likelihood around the maximum posterior of linear modes, which we solve for using optimization methods. By integrating out the modes using perturbative expansion of the likelihood we construct an initial power spectrum estimator, which for a fixed forward model contains all the cosmological information if the initial modes are gaussian distributed. We develop a method to construct the window and covariance matrix such that the estimator is explicitly unbiased and nearly optimal. We then generalize the method to include the forward model parameters, including cosmological and nuisance parameters, and primordial non-gaussianity. We apply the method in the simplified context of nonlinear structure formation, using either simplified 2-LPT dynamics or N-body simulations as the nonlinear mapping between linear and nonlinear density,more » and 2-LPT dynamics in the optimization steps used to reconstruct the initial density modes. We demonstrate that the method gives an unbiased estimator of the initial power spectrum, providing among other a near optimal reconstruction of linear baryonic acoustic oscillations.« less

Authors:
 [1];  [2];  [2];  [2]
  1. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics and Dept. of Physics; Univ. of California, Berkeley, CA (United States). Dept. of Astronomy; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physics Dept.
  2. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics and Dept. of Physics
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1523502
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Cosmology and Astroparticle Physics
Additional Journal Information:
Journal Volume: 2017; Journal Issue: 12; Journal ID: ISSN 1475-7516
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Seljak, Uroš, Aslanyan, Grigor, Feng, Yu, and Modi, Chirag. Towards optimal extraction of cosmological information from nonlinear data. United States: N. p., 2017. Web. doi:10.1088/1475-7516/2017/12/009.
Seljak, Uroš, Aslanyan, Grigor, Feng, Yu, & Modi, Chirag. Towards optimal extraction of cosmological information from nonlinear data. United States. https://doi.org/10.1088/1475-7516/2017/12/009
Seljak, Uroš, Aslanyan, Grigor, Feng, Yu, and Modi, Chirag. Tue . "Towards optimal extraction of cosmological information from nonlinear data". United States. https://doi.org/10.1088/1475-7516/2017/12/009. https://www.osti.gov/servlets/purl/1523502.
@article{osti_1523502,
title = {Towards optimal extraction of cosmological information from nonlinear data},
author = {Seljak, Uroš and Aslanyan, Grigor and Feng, Yu and Modi, Chirag},
abstractNote = {One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics of clusters, density peaks or voids etc.), along with the corresponding covariance matrices. However, this approach is computationally prohibitive and has not been shown to be exhaustive in terms of information content. Here we instead develop a hierarchical Bayesian approach, expanding the likelihood around the maximum posterior of linear modes, which we solve for using optimization methods. By integrating out the modes using perturbative expansion of the likelihood we construct an initial power spectrum estimator, which for a fixed forward model contains all the cosmological information if the initial modes are gaussian distributed. We develop a method to construct the window and covariance matrix such that the estimator is explicitly unbiased and nearly optimal. We then generalize the method to include the forward model parameters, including cosmological and nuisance parameters, and primordial non-gaussianity. We apply the method in the simplified context of nonlinear structure formation, using either simplified 2-LPT dynamics or N-body simulations as the nonlinear mapping between linear and nonlinear density, and 2-LPT dynamics in the optimization steps used to reconstruct the initial density modes. We demonstrate that the method gives an unbiased estimator of the initial power spectrum, providing among other a near optimal reconstruction of linear baryonic acoustic oscillations.},
doi = {10.1088/1475-7516/2017/12/009},
journal = {Journal of Cosmology and Astroparticle Physics},
number = 12,
volume = 2017,
place = {United States},
year = {Tue Dec 05 00:00:00 EST 2017},
month = {Tue Dec 05 00:00:00 EST 2017}
}

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Works referenced in this record:

Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
journal, August 2007

  • Eisenstein, Daniel J.; Seo, Hee‐Jong; Sirko, Edwin
  • The Astrophysical Journal, Vol. 664, Issue 2
  • DOI: 10.1086/518712

Power-spectrum analysis of three-dimensional redshift surveys
journal, May 1994

  • Feldman, Hume A.; Kaiser, Nick; Peacock, John A.
  • The Astrophysical Journal, Vol. 426
  • DOI: 10.1086/174036

Properties and use of CMB power spectrum likelihoods
journal, April 2009


Efficient Wiener filtering without preconditioning
journal, January 2013


Measuring the Galaxy Power Spectrum with Future Redshift Surveys
journal, June 1998

  • Tegmark, Max; Hamilton, Andrew J. S.; Strauss, Michael A.
  • The Astrophysical Journal, Vol. 499, Issue 2
  • DOI: 10.1086/305663

Observational probes of cosmic acceleration
journal, September 2013


Large-scale structure of the Universe and cosmological perturbation theory
journal, September 2002


Cosmography and Power Spectrum Estimation: A Unified Approach
journal, August 1998

  • Seljak, Uroš
  • The Astrophysical Journal, Vol. 503, Issue 2
  • DOI: 10.1086/306019

Iterative initial condition reconstruction
journal, July 2017


Estimating CDM particle trajectories in the mildly non-linear regime of structure formation. Implications for the density field in real and redshift space
journal, December 2012


Past and present cosmic structure in the SDSS DR7 main sample
journal, January 2015

  • Jasche, J.; Leclercq, F.; Wandelt, B. D.
  • Journal of Cosmology and Astroparticle Physics, Vol. 2015, Issue 01
  • DOI: 10.1088/1475-7516/2015/01/036

Interpolation, realization, and reconstruction of noisy, irregularly sampled data
journal, October 1992

  • Rybicki, George B.; Press, William H.
  • The Astrophysical Journal, Vol. 398
  • DOI: 10.1086/171845

How to measure CMB power spectra without losing information
journal, May 1997


Estimators for local non-Gaussianities
journal, March 2007

  • Creminelli, Paolo; Senatore, Leonardo; Zaldarriaga, Matias
  • Journal of Cosmology and Astroparticle Physics, Vol. 2007, Issue 03
  • DOI: 10.1088/1475-7516/2007/03/019

Towards optimal measurement of power spectra - I. Minimum variance pair weighting and the Fisher matrix
journal, August 1997

  • Hamilton, A. J. S.
  • Monthly Notices of the Royal Astronomical Society, Vol. 289, Issue 2
  • DOI: 10.1093/mnras/289.2.285

Estimating the power spectrum of the cosmic microwave background
journal, February 1998


Numerical Optimization
book, January 1999

  • Nocedal, Jorge; Wright, Stephen J.
  • Springer Series in Operations Research and Financial Engineering
  • DOI: 10.1007/b98874

C osmo++: An object-oriented C++ library for cosmology
journal, December 2014


Statistical Analysis of Catalogs of Extragalactic Objects. I. Theory
journal, October 1973

  • Peebles, P. J. E.
  • The Astrophysical Journal, Vol. 185
  • DOI: 10.1086/152431

Towards optimal measurement of power spectra - II. A basis of positive, compact, statistically orthogonal kernels
journal, August 1997

  • Hamilton, A. J. S.
  • Monthly Notices of the Royal Astronomical Society, Vol. 289, Issue 2
  • DOI: 10.1093/mnras/289.2.295

Bayesian physical reconstruction of initial conditions from large-scale structure surveys
journal, April 2013

  • Jasche, Jens; Wandelt, Benjamin D.
  • Monthly Notices of the Royal Astronomical Society, Vol. 432, Issue 2
  • DOI: 10.1093/mnras/stt449

FastPM: a new scheme for fast simulations of dark matter and haloes
journal, August 2016

  • Feng, Yu; Chu, Man-Yat; Seljak, Uroš
  • Monthly Notices of the Royal Astronomical Society, Vol. 463, Issue 3
  • DOI: 10.1093/mnras/stw2123

Myths and truths concerning estimation of power spectra: the case for a hybrid estimator
journal, April 2004


Galaxy-galaxy weak lensing in the Sloan Digital Sky Survey: intrinsic alignments and shear calibration errors
journal, September 2004

  • Hirata, Christopher M.; Mandelbaum, Rachel; Seljak, Uroš
  • Monthly Notices of the Royal Astronomical Society, Vol. 353, Issue 2
  • DOI: 10.1111/j.1365-2966.2004.08090.x

Fast Hamiltonian sampling for large-scale structure inference: Fast Hamiltonian sampling
journal, June 2010


Efficient Wiener filtering without preconditioning
text, January 2012


Large-Scale Structure of the Universe and Cosmological Perturbation Theory
text, January 2001


Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
text, January 2006


Estimating the Power Spectrum of the Cosmic Microwave Background
text, January 1997


Works referencing / citing this record:

Exploring the posterior surface of the large scale structure reconstruction
journal, July 2018

  • Feng, Yu; Seljak, Uroš; Zaldarriaga, Matias
  • Journal of Cosmology and Astroparticle Physics, Vol. 2018, Issue 07
  • DOI: 10.1088/1475-7516/2018/07/043

Cosmological reconstruction from galaxy light: neural network based light-matter connection
journal, October 2018


A rigorous EFT-based forward model for large-scale structure
journal, January 2019

  • Schmidt, Fabian; Elsner, Franz; Jasche, Jens
  • Journal of Cosmology and Astroparticle Physics, Vol. 2019, Issue 01
  • DOI: 10.1088/1475-7516/2019/01/042

Reconstructing large-scale structure with neutral hydrogen surveys
journal, November 2019

  • Modi, Chirag; White, Martin; Slosar, Anže
  • Journal of Cosmology and Astroparticle Physics, Vol. 2019, Issue 11
  • DOI: 10.1088/1475-7516/2019/11/023

Primordial power spectrum and cosmology from black-box galaxy surveys
journal, October 2019

  • Leclercq, Florent; Enzi, Wolfgang; Jasche, Jens
  • Monthly Notices of the Royal Astronomical Society, Vol. 490, Issue 3
  • DOI: 10.1093/mnras/stz2718

Cosmological inference from galaxy-clustering power spectrum: Gaussianization and covariance decomposition
journal, March 2019

  • Wang, Mike (Shengbo); Percival, Will J.; Avila, Santiago
  • Monthly Notices of the Royal Astronomical Society, Vol. 486, Issue 1
  • DOI: 10.1093/mnras/stz829

Improved renormalization group computation of likelihood functions for cosmological data sets
journal, August 2019


Measuring dark matter-neutrino relative velocity on cosmological scales
journal, January 2020


Nonlinear reconstruction of redshift space distortions
journal, February 2018


Recovering lost 21 cm radial modes via cosmic tidal reconstruction
journal, August 2018


ELUCID. V. Lighting Dark Matter Halos with Galaxies
journal, June 2018


TARDIS. I. A Constrained Reconstruction Approach to Modeling the z ∼ 2.5 Cosmic Web Probed by Ly α Forest Tomography
journal, December 2019

  • Horowitz, Benjamin; Lee, Khee-Gan; White, Martin
  • The Astrophysical Journal, Vol. 887, Issue 1
  • DOI: 10.3847/1538-4357/ab4d4c

Nonlinear Reconstruction of the Velocity Field
journal, December 2019


ELUCID V. Lighting dark matter halos with galaxies
text, January 2017


Exploring the posterior surface of the large scale structure reconstruction
text, January 2018


A rigorous EFT-based forward model for large-scale structure
text, January 2018


Cosmological Inference from Galaxy-Clustering Power Spectrum: Gaussianization and Covariance Decomposition
text, January 2018


Nonlinear Reconstruction of the Velocity Field
text, January 2019


Galaxy power-spectrum responses and redshift-space super-sample effect
text, January 2017


FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver
preprint, January 2020


MADLens, a python package for fast and differentiable non-Gaussian lensing simulations
preprint, January 2020


Learning to Simulate High Energy Particle Collisions from Unlabeled Data
text, January 2021