Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers

@article{Ata2014BayesianIO,
  title={Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers},
  author={Metin Ata and Francisco-Shu Kitaura and Volker Muller},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2014},
  volume={446},
  pages={4250-4259}
}
We present a Bayesian reconstruction algorithm to generate unbiased samples of the underlying dark matter field from galaxy redshift data. Our new con tribution consists of implementing a non-Poisson likelihood including a deterministic non-linear and scale-dependent bias. In particular we present the Hamiltonian equations of motions for the negative binomial (NB) probability distribution function. This permits us to efficiently sample the posterior distribution function of density fields given… 

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References

SHOWING 1-10 OF 53 REFERENCES

Bayesian Analysis of Cosmic Structures

We revise the Bayesian inference steps required to analyse the cosmological large-scale structure. Here we place special emphasis on the complications which arise due to the non-Gaussian character of

Multiscale Inference of Matter Fields and Baryon Acoustic Oscillations from the Ly-alpha Forest

We present a novel Bayesian method for the joint reconstruction of cosmological matter density fields, peculiar velocities and power-spectra in the quasi-nonlinear regime. We study its applicability

Bayesian physical reconstruction of initial conditions from large-scale structure surveys

We present a fully probabilistic, physical model of the non-linearly evolved density field, as probed by realistic galaxy surveys. Our model is valid in the linear and mildly non-linear regimes and

Fast Hamiltonian sampling for large‐scale structure inference

A Hamiltonian Monte Carlo (HMC) sampler is employed to obtain samples from a multivariate highly non-Gaussian lognormal Poissonian density posterior given a set of observations to provide an efficient and flexible basis for future high-precision large-scale structure inference.

Recovering the non-linear density field from the galaxy distribution with a Poisson-lognormal filter

We present a general expression for a lognormal filter given an arbitrary non-linear galaxy bias. We derive this filter as the maximum a posteriori solution assuming a lognormal prior distribution

RECONSTRUCTING THE INITIAL DENSITY FIELD OF THE LOCAL UNIVERSE: METHODS AND TESTS WITH MOCK CATALOGS

Our research objective in this paper is to reconstruct an initial linear density field, which follows the multivariate Gaussian distribution with variances given by the linear power spectrum of the

Stochastic Nonlinear Galaxy Biasing

We propose a general formalism for galaxy biasing and apply it to methods for measuring cosmological parameters, such as regression of light versus mass, the analysis of redshift distortions,

The Initial Conditions of the Universe from Constrained Simulations

A new approach to recover the primordial density fluctuations and the cosmic web structure underlying a galaxy distribution and second order Lagrangian Perturbation Theory is used and the presented approach is flexible to adopt any structure formation model.

Modeling Scale-Dependent Bias on the Baryonic Acoustic Scale with the Statistics of Peaks of Gaussian Random Fields

Models of galaxy and halo clustering commonly assume that the tracers can be treated as a continuous field locally biased with respect to the underlying mass distribution. In the peak model pioneered

Non-linear stochastic galaxy biasing in cosmological simulations

We study the biasing relation between dark matter haloes or galaxies and the underlying mass distribution, using cosmological N-body simulations in which galaxies are modelled via semi-analytic
...