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.

Mock galaxy catalogues are essential to the error analysis of cosmological measurements from big galaxy surveys covering thousands of square degrees in the sky, like BOSS, WiggleZ, DES, or Euclid. The PTHalos mock galaxy catalogues were used in the BOSS survey to analyse the BAO measurement from the CMASS (z∼ 0.57) and LOWZ (z∼ 0.32) galaxy samples, which provided the best estimate to date of the cosmic distance scale from galaxy surveys at these redshifts. We review the PTHalos mocks galaxy catalogues and their key contributions to these analyses.

We use a WISE-2MASS-Pan-STARRS1 galaxy catalog to search for a supervoid in the direction of the Cosmic Microwave Background Cold Spot. We obtain photometric redshifts using our multicolor data set to create a tomographic map of the galaxy distribution. The radial density profile centred on the Cold Spot shows a large low density region, extending over 10's of degrees. Motivated by previous Cosmic Microwave Background results, we test for underdensities within two angular radii, 5°, and 15°. Our data, combined with an earlier measurement by Granett et al. 2010, are consistent with a large Rvoid=(192 ± 15)h−1 Mpc (2σ) supervoid with δ ≃ −0.13 ± 0.03 centered at z=0.22 ± 0.01. Such a supervoid, constituting a ∼3.5 σ fluctuation in the ΛCDM model, is a plausible cause for the Cold Spot.

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.

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.

Machine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, called SkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. The SkyNet and BAMBI packages, which are fully parallelised using MPI, are available at http://www.mrao.cam.ac.uk/software/.

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.

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 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.

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.

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).

The coming decade will witness a deluge of data from next generation galaxy surveys such as the Square Kilometre Array and Euclid. How can we optimally and robustly analyse these data to maximise scientific returns from these surveys? Here we discuss recent work in developing both the conceptual and software frameworks for carrying out such analyses and their application to the dark matter halo mass function. We summarise what we have learned about the HMF from the last 10 years of precision CMB data using the open-source HMFcalc framework, before discussing how this framework is being extended to the full Halo Model.

In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.

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.

The recent immense volume of observation data has urged us to recognize solar features and to analyze their correlation with automation methods, and many of these automation methods are closely related to statistical methods. Author investigations the extensive literatures to sum up the function of statistical methods and their advantages in recognizing automatically solar features and analyzing their correlation. The review lists the results and summarizes conclusions of the investigations. The results and conclusions will be helpful to promote the further automation processing.

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 describe an approach to automate the classification of Cepheid variable stars into two subtypes according to their pulsation mode. Automating such classification is relevant to obtain a precise determination of distances to nearby galaxies, which in addition helps reduce the uncertainty in the current expansion of the universe. One main difficulty lies in the compatibility of models trained using different galaxy datasets; a model trained using a training dataset may be ineffectual on a testing set. A solution to such difficulty is to adapt predictive models across domains; this is necessary when the training and testing sets do not follow the same distribution. The gist of our methodology is to train a predictive model on a nearby galaxy (e.g., Large Magellanic Cloud), followed by a model-adaptation step to make the model operable on other nearby galaxies. We follow a parametric approach to density estimation by modeling the training data (anchor galaxy) using a mixture of linear models. We then use maximum likelihood to compute the right amount of variable displacement, until the testing data closely overlaps the training data. At that point, the model can be directly used in the testing data (target galaxy).

It is now common practice to constrain cosmological parameters using supernovae (SNe) catalogues constructed from several different surveys. Before performing such a joint analysis, however, one should check that parameter constraints derived from the individual SNe surveys that make up the catalogue are mutually consistent. We describe a statistically-robust mutual consistency test, which we calibrate using simulations, and apply it to each pairwise combination of the surveys making up, respectively, the UNION2 catalogue and the very recent JLA compilation by Betoule et al. We find no inconsistencies in the latter case, but conclusive evidence for inconsistency between some survey pairs in the UNION2 catalogue.

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 present a Principal Component Analysis (PCA) of the V band light-curves of a sample of more than 100 nearby Core collapse supernovae (CC SNe) from [Anderson et al. (2014)]. We used different reference epochs in order to extract the common properties of these light-curves and searched for correlations to some physical parameters such as the burning of 56Ni, and morphological light-curve parameters such as the length of the plateau, the stretch of the light-curve, and the decrements in brightness after maximum and after the plateau. We also used these similarities to create SNe II light-curve templates that will be used in the future for standardize these objects and determine cosmological distances.