Distance measurements are currently the most powerful tool to study the expansion history of the universe without assuming its matter content nor any theory of gravitation. In general, the reconstruction of the scale factor derivatives, such as the deceleration parameter q(z), is computed using two different methods: fixing the functional form of q(z), which yields potentially biased estimates, or approximating q(z) by a piecewise nth-order polynomial function, whose variance is large. In this work, we address these two methods by reconstructing q(z) assuming only an isotropic and homogeneous universe. For this, we approximate q(z) by a piecewise cubic spline function and, then, we add to the likelihood function a penalty factor, with scatter given by σrel. This factor allows us to vary continuously between the full n knots spline, σrel → ∞, and a single linear function, σrel → 0. We estimate the coefficients of q(z) using the Monte Carlo approach, where the realizations are generated considering ΛCDM as a fiducial model. We apply this procedure in two different cases and assuming four values of σrel to find the best balance between variance and bias. First, we use only the Supernova Legacy Survey 3-year (SNLS3) sample and, in the second analysis, we combine the type Ia supernova (SNeIa) likelihood with those of baryonic acoustic oscillations (BAO) and Hubble function measurements. In both cases we fit simultaneously q(z) and 4 nuisance parameters of the supernovae, namely, the magnitudes $\mathcal{M}$1 and $\mathcal{M}$2 and the light curve parameters α and β.

We describe an innovative statistical approach for the ab initio simultaneous analysis of the formation history and morphology of the large-scale structure of the inhomogeneous Universe. Our algorithm explores the joint posterior distribution of the many millions of parameters involved via efficient Hamiltonian Markov Chain Monte Carlo sampling. We describe its application to the Sloan Digital Sky Survey data release 7 and an additional non-linear filtering step. We illustrate the use of our findings for cosmic web analysis: identification of structures via tidal shear analysis and inference of dark matter voids.

The Euclid space mission proposes to survey 15000 square degrees of the extragalactic sky during 6 years, with a step-and-stare technique. The scheduling of observation sequences is driven by the primary scientific objectives, spacecraft constraints, calibration requirements and physical properties of the sky. We present the current reference implementation of the Euclid survey and on-going work on survey optimization.

The Hard X-ray Modulation Telescope (HXMT) is a Chinese space telescope mission. It is scheduled for launch in 2015. The telescope will perform an all-sky survey in hard X-ray band (1 - 250 keV), a series of deep imaging observations of small sky regions as well as pointed observations. In this work we present a conceptual method to reconstruct light curves from HXMT imaging observation directly, in order to monitor time-varying objects such as GRB, AXP and SGR in hard X-ray band with HXMT imaging observations.

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).

Theories beyond the Standard Model and General Relativity predict a violation of the Weak Equivalence Principle (WEP) just below the current best experimental upper limits. MICROSCOPE (Micro-Satellite à traînée Compensée pour l'Observation du Principe d'Equivalence) will allow us to lower them by two orders of magnitude, and maybe to detect a WEP violation. However, analyzing the MICROSCOPE data will be challenging, mostly because of missing data and a colored noise burrying the signal of interest. In this communication, we apply an inpainting technique to simulated MICROSCOPE data and show that inpainting will help detect a WEP violation signal.

Studying the non-Gaussianity (NG) of the Cosmic Microwave Background (CMB) is an extremely powerful tool to investigate the properties of the very early Universe. The Planck nominal mission CMB maps yielded unprecedented constraints on primordial non-Gaussianity providing with the highest precision test of the standard model of inflation. Planck's high sensitivity also allowed to find evidence for the first time of the late-time non-Gaussianity arising from the Lensing-Integrated Sachs Wolfe (ISW) cross correlation. In this talk I will give details on the Planck data analysis and I will discuss the theoretical implications of the results.

We discuss different methods to separate high- from low-redshift galaxies based on a combination of spectroscopic and photometric observations. Our baseline scenario is the Hobby-Eberly Telescope Dark Energy eXperiment (HETDEX) survey, which will observe several hundred thousand Lyman Alpha Emitting (LAE) galaxies at 1.9 < z < 3.5, and for which the main source of contamination is [OII]-emitting galaxies at z < 0.5. Additional information useful for the separation comes from empirical knowledge of LAE and [OII] luminosity functions and equivalent width distributions as a function of redshift. We consider three separation techniques: a simple cut in equivalent width, a Bayesian separation method, and machine learning algorithms, including support vector machines. These methods can be easily applied to other surveys and used on simulated data in the framework of survey planning.

The presence of multiple fields during inflation might seed a detectable amount of non-Gaussianity in the curvature perturbations, which in turn becomes observable in present data sets like the cosmic microwave background (CMB) or the large scale structure (LSS). Within this proceeding we present a fully analytic method to infer inflationary parameters from observations by exploiting higher-order statistics of the curvature perturbations. To keep this analyticity, and thereby to dispense with numerically expensive sampling techniques, a saddle-point approximation is introduced whose precision has been validated for a numerical toy example. Applied to real data, this approach might enable to discriminate among the still viable models of inflation.

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.

Anomalies drive scientific discovery – they are associated with the cutting edge of the research frontier, and thus typically exploit data in the low signal-to-noise regime. In astronomy, the prevalence of systematics –- both “known unknowns” and “unknown unknowns” – combined with increasingly large datasets, the widespread use of ad hoc estimators for anomaly detection, and the “look-elsewhere” effect, can lead to spurious false detections. In this informal note, I argue that anomaly detection leading to discoveries of new physics requires a combination of physical understanding, careful experimental design to avoid confirmation bias, and self-consistent statistical methods. These points are illustrated with several concrete examples from cosmology.

For SDSS quasar data (2005) we have truncated data structure whereas for the survey of 2007 the data is no longer truncated. This calls for development or use of completely different statistical methodology to study the data for the evolution of the same objects like quasars. These different methodologies suggest different interpretation for a particular phenomenon in nature. This leads to the issue of validation of the data. More intriguing and challenging issue crops up as, given all of the data, what can be said about the laws of physics that have been operating over the universe? Over here we have used the concept of Neural Network to model the relationship between redshift and apparent magnitude.

We present accurate measurements of the large-scale clustering of photometric quasars from the Sloan Digital Sky Survey. These results, detailed in Leistedt & Peiris (2014), rely on a novel technique to identify and treat systematics when measuring angular power spectra, using null-tests and analytical marginalisation. This approach can be used to maximise the extraction of information from current and future galaxy or quasar surveys. For example, it enables to robustly constrain primordial non-Gaussianity (PNG), which modifies the bias of galaxies and quasars on large scales – the most sensitive to observational systematics. The constraints on PNG obtained with the quasar power spectra are detailed in Leistedt, Peiris & Roth (2014); these are the most stringent constraints to date obtained with a single tracer of the large-scale structure.

From the galaxy power spectrum in redshift space, we derive semi-analytical results on the generic degeneracy of galaxy clustering measurements. Defining the observables A = Gbσ8 and R = Gfσ8, (being G the growth function, b the bias, f the growth rate, and σ8 the amplitude of the power spectrum), we perform a Fisher matrix formalism to forecast the expected precision of these quantities for a Euclid-like survey. Among the results we found that galaxy surveys have generically a slightly negative correlation between A and R, and they can always measure R about 3.7 to 4.7 times better than A.

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.

The success of many cosmological surveys in the near future is highly grounded on the quality of their photometry. The Javalambre-PAU Astrophysical Survey (J-PAS) will image more than 8500 deg2 of the Northern Sky Hemisphere in 54 narrow + 2 medium/broad optical bands plus Sloan u, g and r bands. The main goal of J-PAS is to provide the best constrains on the cosmological parameters before the arrival of projects like Euclid or LSST. To achieve this goal the uncertainty in photo-z cannot be larger than 0.3% for several millions of galaxies and this is highly dependent on the photometric accuracy.

The photometric calibration of J-PAS will imply the intensive use of huge amounts of data and the use of statistical tools is unavoidable. Here, we present some of the key steps in the photometric calibration of J-PAS that will demand a suitable statistical approach.

We analyze the cross-correlation function of the soft and hard X-rays of the atoll source 4U 1636-53 with RXTE data. The results show that the cross-correlations evolve along the different branches of the color-color diagram. At the lower left banana states, we have both positive and ambiguous correlations, and positive correlations are dominant for the lower banana and the upper banana states. The anti-correlation is detected at the top of the upper banana states. The cross-correlations of two atoll sources 4U 1735-44 and 4U 1608-52 have been studied in previous work, and the anti-correlations are detected at the lower left banana or the top of the upper banana states. Our results show that, in the 4U 1636-53, the distribution of the cross-correlations in the color-color diagram is similar to those of 4U 1735-44 and 4U 1608-52, and confirm further that the distribution of cross-correlations in color-color diagram could be correlated with the luminosity of the source.

Comparisons between observed and predicted strong lensing properties of galaxy clusters have been used to claim either tension or consistency with ΛCDM cosmology. However, standard approaches to such tests are unable to quantify the preference for one cosmology over another. We advocate a Bayesian approach whereby the parameters defining the scaling relation between Einstein radii and cluster mass are treated as the observables. We demonstrate a method of estimating the likelihood for observing these parameters under the ΛCDM framework, using the X-ray selected z > 0.5 MACS clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We account for cluster lens triaxiality within the modelling of the likelihood function. Cluster selection criteria is found to play as important a role as the uncertainty related to the description of star formation and feedback.

The proof corrections supplied by the author were not made to the final version of the article (Guarnizo, Amendola, Kunz and Vollmer, 2015).

We apologise to the authors and to the readers for this omission.

After (2.6) on page 348, the statement “being S = AR.” should read

“being S = AR. and P1 = R/A.”

Improved versions of the figures are also presented below.