Abstract
ABSTRACT We present a Bayesian hierarchical modelling approach to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data. This method uses a physical model of cosmic structure formation to infer physically plausible cosmic structures, which accounts for the non-Gaussian features of the gravitationally evolved matter distribution and light-cone effects. We test and validate our framework with realistic simulated shear data, demonstrating that the method recovers the unbiased matter distribution and the correct lensing and matter power spectrum. While the cosmology is fixed in this test, and the method employs a prior power spectrum, we demonstrate that the lensing results are sensitive to the true power spectrum when this differs from the prior. In this case, the density field samples are generated with a power spectrum that deviates from the prior, and the method recovers the true lensing power spectrum. The method also recovers the matter power spectrum across the sky, but as currently implemented, it cannot determine the radial power since isotropy is not imposed. In summary, our method provides physically plausible inference of the dark matter distribution from cosmic shear data, allowing us to extract information beyond the two-point statistics and exploiting the full information content of the cosmological fields.
Highlights
As light from distant galaxies propagates through the Universe, it is deflected by the gravitational field induced by the large-scale structures
We build on the Bayesian Origin Reconstruction from Galaxies (BORG, Jasche & Kitaura 2010a; Jasche & Wandelt 2013a; Lavaux et al 2019) framework, which employs a physical description of the dark matter dynamics and allows us to sample from the initial conditions, which are accurately described by Gaussian statistics
We have developed a Bayesian physical forward model to infer the matter density field and primordial fluctuations from cosmic shear data
Summary
As light from distant galaxies propagates through the Universe, it is deflected by the gravitational field induced by the large-scale structures. Many of the current cosmic shear analyses focus on extracting information from the correlation function or the associated power spectrum (Kitching et al 2011; Heymans et al 2013; Kitching et al 2014, 2015; Alsing et al 2016; Kitching et al 2016; Hildebrandt et al 2017; Troxel et al 2018; Hikage et al 2019; Taylor et al 2019) These analyses capture the two-point statistics, but they do not fully capture the non-Gaussian information encoded in the filamentary features of the matter distribution We build on the Bayesian Origin Reconstruction from Galaxies (BORG, Jasche & Kitaura 2010a; Jasche & Wandelt 2013a; Lavaux et al 2019) framework, which employs a physical description of the dark matter dynamics and allows us to sample from the initial conditions, which are accurately described by Gaussian statistics With this more complex data model, we get a better representation of the data, and we can extract information beyond the two-point statistics, exploiting the full information content of the shear fields.
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