A new fully non-linear reconstruction algorithm, based on the least-action principle and extending the Fast Action Minimisation method by is presented, intended for applications with the next-generation massive spectroscopic surveys. Its capability of recovering the velocity field starting from the observed density field is tested on dark-matter halo catalogues simulation to trace the trajectories of up to 10^6 haloes backward-in-time. Both in real and redshift-space it successfully recovers the peculiar velocities. The new algorithm is first employed for the accurate recovery of the Baryonic Acoustic Oscillations (BAO) scale in two-point correlation functions. Tests on dark-matter halo catalogues show how the new algorithm successfully recovers the BAO feature in real and redshift-space, also for anomalous samples showing misplaced or absent signature of BAO. A comparison with the first-order Lagrangian reconstruction is presented, showing that this techniques outperforms the linear approximation in recovering an unbiased measurement of the BAO scale. A second version of the algorithm accounting for the survey geometry and the bias of tracers is finally tested on low-redshift galaxy samples extracted form mocks specifically designed to match the SDSS-DR12 LRG clustering. The analysis of the anisotropic clustering indicates the non-linear reconstruction as a fundamental tool to brake the degeneracy between redshift-space distortion and the Alcock-Paczynski effect. Finally the application to the cosmic voids analysis is introduced, showing the great potentiality of a non-linear modelling of the velocity field in restoring the intrinsic isotropy of voids.

Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.

The amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe.

Since 1963, the Texas Symposia have been a biennial, peripatetic forum for forefront developments on a wide range of topics in relativistic astrophysics, from pulsars to string theory, from the birth of the universe to the death of stars. The 26 plenary lectures, 230 parallel session talks and 265 poster presentations attest to the scientific vitality of this interdisciplinary field.