The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statistical inference can be done in the reduced space via probability distribution estimation and manifold estimation.

We present results from an end-to-end simulation pipeline of interferometric observations of cosmic microwave background polarization. We use both maximum-likelihood and Gibbs sampling techniques to estimate the power spectrum. In addition, we use Gibbs sampling for image reconstruction from interferometric visibilities. The results indicate the level to which various systematic errors (e.g., pointing errors, gain errors, beam shape errors, cross polarization) must be controlled in order to successfully detect and characterize primordial B modes and achieve other scientific goals. In addition, we show that Gibbs sampling is an effective method of image reconstruction for interferometric data in other astrophysical contexts.

A range of Bayesian tools has become widely used in cosmological data treatment and parameter inference (see Kunz et al.2007, Trotta 2008, Amendola et al.2013). With increasingly big datasets and higher precision, tools that enable us to further enhance the accuracy of our measurements gain importance. Here we present an approach based on internal robustness, introduced in Amendola et al. (2013) and adopted in Heneka et al. (2014), to identify biased subsets of data and hidden correlation in a model independent way.

This paper is concerned with a meteoroid shape estimation technique based on statistical laws of distribution for fragment masses. The idea is derived from the experiments that show that brittle fracturing produces multiple fragments of size lesser than or equal to the least dimension of the body. The number of fragments depends on fragment masses as a power law with exponential cutoff. The scaling exponent essentially indicates the initial form of the fragmented body. We apply the technique of scaling analysis to the empirical data on the mass distributions for Košice, Almahata Sitta and Bassikounou meteorites.

The main feature of the spatial large-scale galaxy distribution is its intricate network of galaxy filaments. This network is spanned by the galaxy locations that can be interpreted as a three-dimensional point distribution. The global properties of the point process can be measured by different statistical methods, which, however, do not describe directly the structure elements. The morphology of the large-scale structure, on the other hand, is an important property of the galaxy distribution. Here, we apply an object point process with interactions (the Bisous model) to trace and extract the filamentary network in the presently largest galaxy redshift survey, the Sloan Digital Sky Survey (SDSS data release 10). We search for multi-scale filaments in the galaxy distribution that have a radius of about 0.5, 1.0, 2.0, and 4.0 h−1 Mpc. We extract the spines of the filamentary network and divide the detected network into single filaments.

I discuss how the cosmographic approach to the determination of cosmological parameters can be implemented with the inclusion of high-redshift data. I argue on the viability of such high-z probes for cosmographic purposes, and resume some statistical issues in finding the most reliable cosmographic truncation.

A new generation of radio telescopes with unprecedented capabilities for astronomy and fundamental physics will be in operation over the next few years. With high sensitivities and large fields of view, they are ideal for cosmological applications. We discuss their uses for cosmology focusing on the observational technique of HI intensity mapping, in particular at low redshifts (z < 4). This novel observational window promises to bring new insights for cosmology, in particular on ultra-large scales and at a redshift range that can go beyond the dark energy domination epoch. In terms of standard constraints on the dark energy equation of state, telescopes such as Phase I of the SKA should be able to obtain constrains about as well as a future galaxy redshift surveys. Statistical techniques to deal with foregrounds and calibration issues, as well as possible systematics are also discussed.

We revisit recent claims of significant mirror-parity in CMB data from WMAP and Planck with a careful analysis using statistical estimators in both harmonic and pixel spaces. While the data indeed show significant signs of odd mirror-parity under some circumstances, the broad study shows that the results are not in significant tension with ΛCDM.

Cosmological weak lensing is the powerful probe of cosmology. Here we address one of the most fundamental, statistical questions inherent in weak lensing cosmology: whether or not we can recover the initial Gaussian information content of large-scale structure by combining the weak lensing observables, here focused on the weak lensing power spectrum and bispectrum. To address this question we fully take into account correlations between the power spectra of different multipoles and the bispectra of different triangle configurations, measured from a finite area survey. In particular we show that super-survey modes whose length scale is larger than or comparable with the survey size cause significant sample variance in the weak lensing correlations via the mode-coupling with sub-survey modes due to nonlinear gravitational clustering – the so-called super-sample variance. In this paper we discuss the origin of the super-sample variance and then study the information content inherent in the weak lensing correlation functions up to three-point level.

We present a new galaxy survey simulation package, which combines the power of Markov Chain Monte Carlo (MCMC) sampling with a robust and adaptable model of galaxy evolution. The aim of this code is to aid in the characterization and study of new and existing galaxy surveys. In this paper we briefly describe the MCMC implementation and the survey simulation methodology and associated tools. A test case of this full suite was to constrain the evolution of the IR Luminosity Function (LF) based on the HerMES (Herschel SPIRE) survey of the Spitzer First Look Survey field. The initial results are consistent with previous studies, but our more general approach should be of wider benefit to the community.

Large photometric galaxy surveys allow the study of questions at the forefront of science, such as the nature of dark energy. The success of such surveys depends on the ability to measure the photometric redshifts of objects (photo-zs), based on limited spectral data. A new major version of the public photo-z estimation software, ANNz, is presented here. The new code incorporates several machine-learning methods, such as artificial neural networks and boosted decision/regression trees, which are all used in concert. The objective of the algorithm is to dynamically optimize the performance of the photo-z estimation, and to properly derive the associated uncertainties. In addition to single-value solutions, the new code also generates full probability density functions in two independent ways.

We propose a Bayesian method to measure the total Galactic extinction parameters, RV and AV. Validation tests based on the simulated data indicate that the method can achieve the accuracy of around 0.01 mag. We apply this method to the SDSS BHB stars in the northern Galactic cap and find that the derived extinctions are highly consistent with those from Schlegel et al. (1998). It suggests that the Bayesian method is promising for the extinction estimation, even the reddening values are close to the observational errors.

Observational data on clusters of galaxies holds relevant information that can be used to determine the relative plausibility of different models for the large-scale evolution of the Universe, or estimate the joint posterior probability distribution function of the parameters that pertain to each model. Within the next few years, several surveys of the sky will yield large galaxy cluster catalogues. In order to make use of the vast amount of information they will contain, their selection functions will have to be properly understood. We argue this, as well as the estimation of the full joint posterior probability distribution function of the most relevant cluster properties, can be best achieved in the framework of bayesian statistics.

In the statistical framework of likelihood-free inference, the posterior distribution of model parameters is explored via simulation rather than direct evaluation of the likelihood function, permitting inference in situations where this function is analytically intractable. We consider the problem of estimating cosmological parameters using measurements of the weak gravitational lensing of galaxies; specifically, we propose the use a likelihood-free approach to investigate the posterior distribution of some parameters in the ΛCDM model upon observing a large number of sheared galaxies. The choice of summary statistic used when comparing observed data and simulated data in the likelihood-free inference framework is critical, so we work toward a principled method of choosing the summary statistic, aiming for dimension reduction while seeking a statistic that is as close as possible to being sufficient for the parameters of interest.

In 2008, Granett et al. claimed a direct detection of the integrated Sachs- Wolfe (iSW) effect by a stacking approach of patches of the CMB, at the positions of identified superstructures. However, the high amplitude of their measured signal seems to be at odds with predictions from the standard model of cosmology. However, multiple questions arise from these results and their expected value : I propose here an original theoretical prediction of the iSW effect produced by such superstructures. I use simulations based on GR and the LTB metric to reproduce cosmic structures and predict their exact full theoretical iSW effect. Expected amplitudes are consistent with the measured signal ; however the latter shows non-reproducible features that are hardly compatible with ΛCDM predictions.

We study the possibilities of using line-of-sight projected luminosity density and filament probability fields to find the best locations in the sky to attempt to observe the elusive Warm-Hot Intergalactic Matter (WHIM). We calculate galaxy luminosity fields on a regular grid and then project those to the plane of sky. Column densities are then found by integrating along multiple lines of sight. We also create the projections of the filament probability fields. The results are presented as sky maps of the potentially suitable areas and the corresponding fractions of sky coverage.

We investigate the contribution of a local over- or under-density to linear estimates of the cosmic dipole. We focus on radio continuum surveys. Recently it was shown that the radio dipole amplitude is larger than expected from the corresponding dipole of the CMB. We show that a significant contribution to this excess could come from local structure.

We match the XMM-Newton 3XMMi-DR4 catalog with the Sloan Digital Sky Survey (SDSS) Data Release 10 and the United Kingdom Infrared Deep Sky Survey (UKIDSS) Data Release 9. Based on this X-ray/optical/infrared catalog, we probe the distribution of various types of X-ray emitters in the multidimensional parameter space. It is found that quasars, galaxies and stars have some kind distribution rule, especially for stars. The result shows that only using the X-ray/optical features, stars are difficult to discriminate from galaxies and quasars, the added information from infrared band is very helpful to improve the classification result of any classifier. Comparing the classification accuracy of random forests with that of rotation forests, rotation forests show better performance.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) project performed its five year formal survey since Sep. 2012, already fulfilled the pilot survey and the 1st two years general survey with an output - spectroscopic data archive containing more than 4.1 million observations. One of the scientific objectives of the project is for better understanding the structure and evolution of the Milky Way. Thus, credible derivation of the physical properties of the stars plays a key role for the exploration. We developed and implemented the LAMOST stellar parameter pipeline (LASP) which can automatically determine the fundamental stellar atmospheric parameters (effective temperature Teff, surface gravity log g, metallicity [Fe/H], radial velocity Vr) for late A, FGK type stars observed during the survey. An overview of the LASP, including the strategy, the algorithm and the process is presented in this work.