Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization

We address the inverse problem of cosmic large-scale structure reconstruction from a Bayesian perspective. For a linear data model, a number of known and novel reconstruction schemes, which differ in terms of the underlying signal prior, data likelihood, and numerical inverse extra-regularization schemes are derived and classified. The Bayesian methodology presented in this paper tries to unify and extend the following methods: Wiener-filtering, Tikhonov regularization, Ridge regression, Maximum Entropy, and inverse regularization techniques. The inverse techniques considered here are the asymptotic regularization, the Jacobi, Steepest Descent, Newton-Raphson, Landweber-Fridman, and both linear and non-linear Krylov methods based on Fletcher-Reeves, Polak-Ribiere, and Hestenes-Stiefel Conjugate Gradients. The structures of the up-to-date highest-performing algorithms are presented, based on an operator scheme, which permits one to exploit the power of fast Fourier transforms. Using such an implementation of the generalized Wiener-filter in the novel ARGO-software package, the different numerical schemes are benchmarked with 1-, 2-, and 3-dimensional problems including structured white and Poissonian noise, data windowing and blurring effects. A novel numerical Krylov scheme is shown to be superior in terms of performance and fidelity. These fast inverse methods ultimately will enable the application of sampling techniques to explore complex joint posterior distributions. We outline how the space of the dark-matter density field, the peculiar velocity field, and the power spectrum can jointly be investigated by a Gibbs-sampling process. Such a method can be applied for the redshift distortions correction of the observed galaxies and for time-reversal reconstructions of the initial density field.

Dynamics of shock propagation and nucleosynthesis conditions in O-Ne-Mg core supernovae

It has been recently proposed that the shocked surface layers of exploding O-Ne-Mg cores provide the conditions for r-process nucleosynthesis, because their rapid expansion and high entropies enable heavy r-process isotopes to form even in an environment with very low initial neutron excess of the matter. We show here that the most sophisticated available hydrodynamic simulations (in spherical and axial symmetry) do not support this new r-process scenario because they fail to provide the necessary conditions of temperature, entropy, and expansion timescale by significant factors. This suggests that, either the formation of r-process elements works differently than suggested by Ning et al. (2007, NQM07), or that some essential core properties with influence on the explosion dynamics might be different from those predicted by Nomotos progenitor model.