Models for galaxy clusters abundance and their spatial distribution are sensitive to cosmological parameters. Present and future surveys will provide high-redshift sample of clusters, such as Dark Energy Survey (z ⩽ 1.3), making cluster number counts one of the most promising cosmological probes. In the literature, some cosmological analyses are carried out using small cluster catalogs (tens to hundreds), like in Sunyaev-Zel'dovich (SZ) surveys. However, it is not guaranteed that maximum likelihood estimators of cosmological parameters are unbiased in this scenario. In this work we study different estimators of the cold dark matter density parameter Ωc, σ8 and the dark energy equation of state parameter w0 obtained from cluster abundance. Using an unbinned likelihood for cluster number counts and the Monte Carlo approach, we determine the presence of bias and how it varies with the size of the sample. Our fiducial models are based on the South Pole Telescope (SPT). We show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the SPT sensitivity.

The evidence for the acceleration of the universe shows that canonical theories of cosmology and particle physics are incomplete, and that new physics is out there, waiting to be discovered. Forthcoming high-resolution ultra-stable spectrographs will play a key role in this quest for new physics. Here we focus on astrophysical tests of the stability of nature's fundamental couplings, and by taking existing VLT data as a starting point we discuss how forthcoming improvements (in particular with the E-ELT) will impact on fundamental cosmology.

Using transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

The cosmic web is the intricate network of filaments outlined by the galaxies positions distribution in our Universe. One possible manner to break the complexity of such an elaborate geometrical structure is to assume it made of simple interacting objects. Under this hypothesis, the filamentary network can be considered as the realization of an object or a marked point process. These processes are probabilistic models dealing with configurations of random objects given by random points having random characteristics or marks. Here, the filamentary network is considered as the realization of such a process, with the objects being cylinders that align and connect in order to form the network. The paper presents the use of marked point processes to the detection and the characterization of the galactic filaments.

The general problem of the statistics of the primordial curvature perturbation field in cosmology is reviewed. The search for non-Gaussian signatures in cosmological perturbations, originated from inflation in the early Universe is discussed both from the theoretical point of view and in connection with constraints coming from recent observations and future prospects for observing/constraining them.

Euclid is the next ESA mission devoted to cosmology. It aims at observing most of the extragalactic sky, studying both gravitational lensing and clustering over ~15,000 square degrees. The mission is expected to be launched in year 2020 and to last six years. The sheer amount of data of different kinds, the variety of (un)known systematic effects and the complexity of measures require efforts both in sophisticated simulations and techniques of data analysis. We review the mission main characteristics, some aspects of the the survey and highlight some of the areas of interest to this meeting.

The origin of large-scale magnetic fields is an unsolved problem in cosmology. In order to overcome, a possible scenario comes from the idea that these fields emerged from a small primordial magnetic field (PMF), produced in the early universe. This field could lead to the observed large-scales magnetic fields but also, would have left an imprint on the cosmic microwave background (CMB). In this work we summarize some statistical properties of this PMFs on the FLRW background. Then, we show the resulting PMF power spectrum using cosmological perturbation theory and some effects of PMFs on the CMB anisotropies.

The primordial power spectrum is an indirect probe of inflation or other structure-formation mechanisms. We introduce a new method, named PRISM, to estimate this spectrum from the empirical cosmic microwave background (CMB) power spectrum. This is a sparsity-based inversion method, which leverages a sparsity prior on features in the primordial spectrum in a wavelet dictionary to regularise the inverse problem. This non-parametric approach is able to reconstruct the global shape as well as localised features of the primordial spectrum accurately and proves to be robust for detecting deviations from the currently favoured scale-invariant spectrum. We investigate the strength of this method on a set of WMAP nine-year simulated data for three types of primordial spectra and then process the WMAP nine-year data as well as the Planck PR1 data. We find no significant departures from a near scale-invariant spectrum.

The integrated Sachs-Wolfe effect was recently detected at a level of 4.4σ by [Granett et al. (2008)], by stacking compensated CMB temperature patches corresponding to superstructures in the universe. We test the reported signal using realistic gaussian random realizations of the CMB sky, based on the temperature power spectrum predicted by the concordance ΛCDM model. Such simulations provide a complementary approach to the largely used N-body simulations and allow to include the contaminant effects due to small-scale temperature fluctuations. We also apply our pipeline to foreground-cleaned CMB sky maps using the [Granett et al. (2008)] voids/clusters catalog. We confirm the detection of a signal, which depart from the null hypothesis by 3.5σ, and we report a tension with our theoretical estimates at a significance of about 2.5σ.

Star clusters are among the fundamental astrophysical objects used in setting the local distance scale. Despite its crucial importance, the accurate determination of the distances to the Magellanic Clouds (SMC/LMC) remains a fuzzy step in the cosmological distance ladder. The exquisite astrometry of the recently launched ESA Gaia mission is expected to deliver extremely accurate statistical parallaxes, and thus distances, to the SMC/LMC. However, an independent SMC/LMC distance determination via main sequence fitting of star clusters provides an important validation check point for the Gaia distances. This has been a valuable lesson learnt from the famous Hipparcos Pleiades distance discrepancy problem. Current observations will allow hundreds of LMC/SMC clusters to be analyzed in this light.

Today, the most common approach for star cluster main sequence fitting is still by eye. The process is intrinsically subjective and affected by large uncertainties, especially when applied to poorly populated clusters. It is also, clearly, not an efficient route for addressing the analysis of hundreds, or thousands, of star clusters. These concerns, together with a new attitude towards advanced statistical techniques in astronomy and the availability of powerful computers, have led to the emergence of software packages designed for analyzing star cluster photometry. With a few rare exceptions, those packages are not publicly available.

Here we present OCAAT (Open Cluster Automated Analysis Tool), a suite of publicly available open source tools that fully automatises cluster isochrone fitting. The code will be applied to a large set of hundreds of open clusters observed in the Washington system, located in the Milky Way and the Magellanic Clouds. This will allow us to generate an objective and homogeneous catalog of distances up to ~ 60 kpc along with its associated reddening, ages and metallicities and uncertainty estimates.

The standard Bayesian model formalism comparison cannot be applied to most cosmological models as they lack well-motivated parameter priors. However, if the data-set being used is separable, then it is possible to use some of the data to obtain the necessary parameter distributions, the rest of the data being retained for model comparison. While such methods are not fully prescriptive, they provide a route to applying Bayesian model comparison in cosmological situations where it could not otherwise be used.

The problem of supernova photometric identification is still an open issue faced by large photometric surveys. In a previous investigation, we showed how combining Kernel Principal Component Analysis and Nearest Neighbour algorithms enable us to photometrically classify supernovae with a high rate of success. In the present work, we demonstrate that the introduction of Gaussian Process Regression (GPR) in determining each light curve highly improves the efficiency and purity rates. We present detailed comparison with results from the literature, based on the same simulated data set. The method proved to be satisfactorily efficient, providing high purity (⩽ 96%) rates when compared with standard algorithms, without demanding any information on astrophysical properties of the local environment, host galaxy or redshift.

We describe an analysis of 3-GHz confusion-limited data from the Karl J. Jansky Very Large Array (VLA). We show that with minimal model assumptions, P(D), Bayesian and Markov-Chain Mone-Carlo (MCMC) methods can define the source count to levels some 10 times fainter than the conventional confusion limit. Our verification process includes a full realistic simulation that considers known information on source angular extent and clustering. It appears that careful analysis of the statistical properties of an image is more effective than counting individual objects.

With the advent of wide-field surveys, cosmology has entered a new golden age of data where our cosmological model and the nature of dark universe will be tested with unprecedented accuracy, so that we can strive for high precision cosmology. Observational probes like weak lensing, galaxy surveys and the cosmic microwave background as well as other observations will all contribute to these advances. These different probes trace the underlying expansion history and growth of structure in complementary ways and can be combined in order to extract cosmological parameters as best as possible. With future wide-field surveys, observational overlap means these will trace the same physical underlying dark matter distribution, and extra care must be taken when combining information from different probes. Consideration of probe combination is a fundamental aspect of cosmostatistics and important to ensure optimal use of future wide-field surveys.

We propose and preliminarily implement a data-mining based platform to assist experts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and it is VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR.

We briefly present a new coordinate-invariant statistical test dedicated to the study of the orientations of transverse quantities of non-uniformly distributed sources on the celestial sphere. These quantities can be projected spin-axes or polarization vectors of astronomical sources.

We present a method to map multivariate non-Gaussian posterior probability densities into Gaussian ones via nonlinear Box-Cox transformations, and generalizations thereof. This is analogous to the search for normal parameters in the CMB, but can in principle be applied to any probability density that is continuous and unimodal. The search for the optimally Gaussianizing transformation amongst the Box-Cox family is performed via a maximum likelihood formalism. We can judge the quality of the found transformation a posteriori: qualitatively via statistical tests of Gaussianity, and more illustratively by how well it reproduces the credible regions. The method permits an analytical reconstruction of the posterior from a sample, e.g. a Markov chain, and simplifies the subsequent joint analysis with other experiments. Furthermore, it permits the characterization of a non-Gaussian posterior in a compact and efficient way. The expression for the non-Gaussian posterior can be employed to find analytic formulae for the Bayesian evidence, and consequently be used for model comparison.

The global shape, or topology, of the universe is not constrained by the equations of General Relativity, which only describe the local universe. As a consequence, the boundaries of space are not fixed and topologies different from the trivial infinite Euclidean space are possible. The cosmic microwave background (CMB) is the most efficient tool to study topology and test alternative models. Multi-connected topologies, such as the 3-torus, are of great interest because they are anisotropic and allow us to test a possible violation of isotropy in CMB data. We show that the correlation function of the coefficients of the expansion of the temperature and polarization anisotropies in spherical harmonics encodes a topological signature. This signature can be used to distinguish an infinite space from a multi-connected space on sizes larger than the diameter of the last scattering surface (DLSS). With the help of the Kullback-Leibler divergence, we set the size of the edge of the biggest distinguishable torus with CMB temperature fluctuations and E-modes of polarization to 1.15 DLSS. CMB temperature fluctuations allow us to detect universes bigger than the observable universe, whereas E-modes are efficient to detect universes smaller than the observable universe.

We present a new photometric typing technique for normal type Ia supernovae (SNe~Ia) and peculiar SNe~Ia such as SN~1991bg-like, type Iax and super-Chandrasekhar SNe~Ia. This photometric classifier allows the identification of exclusively normal SNe~Ia (with a purity above 99% and an efficiency greater than 80%) in current and future large wide field surveys for cosmological studies.

We analyse the correlations between continuum properties and emission line equivalent widths of star-forming and narrow-line active galaxies from SDSS. We show that emission line strengths can be predicted reasonably well from PCA coefficients of the stellar continuum using local multiple linear regression. Since upcoming sky surveys will make broadband observations only, theoretical modelling of spectra will be essential to estimate physical properties of galaxies. Combined with stellar population synthesis models, our technique will help generate more accurate model spectra and mock catalogues of galaxies to be used to fit data from new surveys. We also show that, by combining PCA coefficients from the pure continuum and the emission lines, a plausible distinction can be made between weak AGNs and quiescent star-forming galaxies. Our method uses a support vector machine, and allows a more refined separation of active and star-forming galaxies than the empirical curve of Kauffmann et al. (2003).