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Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7
J. Jasche, F. S. Kitaura, C. Li +1
In this work we present the first non-linear, non-Gaussian full Bayesian large scale structure analysis of the cosmic density field conducted so far. The density inference is based…
Fast Hamiltonian sampling for large scale structure inference
J. Jasche, F. S. Kitaura
In this work we present a new and efficient Bayesian method for nonlinear three dimensional large scale structure inference. We employ a Hamiltonian Monte Carlo (HMC) sampler to ob…
Bayesian power-spectrum inference for Large Scale Structure data
J. Jasche, F. S. Kitaura, B. D. Wandelt +1
We describe an exact, flexible, and computationally efficient algorithm for a joint estimation of the large-scale structure and its power-spectrum, building on a Gibbs sampling fra…
Recovering the nonlinear density field from the galaxy distribution with a Poisson-Lognormal filter
Francisco S. Kitaura, Jens Jasche, R. Benton Metcalf
We present a general expression for a lognormal filter given an arbitrary nonlinear galaxy bias. We derive this filter as the maximum a posteriori solution assuming a lognormal pri…
Cosmic Cartography of the Large-Scale Structure with Sloan Digital Sky Survey Data Release 6
Francisco S. Kitaura, Jens Jasche, Cheng Li +5
We present the largest Wiener reconstruction of the cosmic density field made to date. The reconstruction is based on the Sloan Digital Sky Survey data release 6 covering the north…
Digital Signal Processing in Cosmology
J. Jasche, F. S. Kitaura, T. A. Ensslin
We address the problem of discretizing continuous cosmological signals such as a galaxy distribution for further processing with Fast Fourier techniques. Discretizing, in particula…