UV slope of z∼\sim3 bright (L>L∗L>L^{*}) Lyman-break galaxies in the COSMOS field

We analyse a unique sample of 517 bright ($L>L^{*}$) LBGs at redshift z$\sim$3 in order to characterise the distribution of their UV slopes $\beta$ and infer their dust extinction under standard assumptions. We exploited multi-band observations over 750 arcmin$^2$ of the COSMOS field that were acquired with three different ground-based facilities: the Large Binocular Camera (LBC) on the Large Binocular Telescope (LBT), the Suprime-Cam on the SUBARU telescope, and the VIRCAM on the VISTA telescope (ULTRAVISTA DR2). Our multi-band photometric catalogue is based on a new method that is designed to maximise the signal-to-noise ratio in the estimate of accurate galaxy colours from images with different point spread functions (PSF). We adopted an improved selection criterion based on deep Y-band data to isolate a sample of galaxies at $z\sim 3$ to minimise selection biases. We measured the UV slopes ($\beta$) of the objects in our sample and then recovered the intrinsic probability density function of $\beta$ values (PDF($\beta$)), taking into account the effect of observational uncertainties through detailed simulations. The galaxies in our sample are characterised by mildly red UV slopes with $\simeq -1.70$ throughout the enitre luminosity range that is probed by our data ($-24\lesssim M_{1600}\lesssim -21$). The resulting dust-corrected star formation rate density (SFRD) is $log(SFRD)\simeq-1.6 M_{\odot}/yr/Mpc^{3}$, corresponding to a contribution of about 25% to the total SFRD at z$\sim$3 under standard assumptions. Ultra-bright LBGs at $z \sim 3$ match the known trends, with UV slopes being redder at decreasing redshifts, and brighter galaxies being more highly dust extinct and more frequently star-forming than fainter galaxies. [abridged]Comment: Matched to journal version. 11 pages, 13 figures, Astronomy & Astrophysics in pres

Dissecting the Gravitational Lens B1608+656. II. Precision Measurements of the Hubble Constant, Spatial Curvature, and the Dark Energy Equation of State

Strong gravitational lens systems with measured time delays between the multiple images provide a method for measuring the "time-delay distance" to the lens, and thus the Hubble constant. We present a Bayesian analysis of the strong gravitational lens system B1608+656, incorporating (i) new, deep Hubble Space Telescope (HST) observations, (ii) a new velocity dispersion measurement of 260+/-15 km/s for the primary lens galaxy, and (iii) an updated study of the lens' environment. When modeling the stellar dynamics of the primary lens galaxy, the lensing effect, and the environment of the lens, we explicitly include the total mass distribution profile logarithmic slope gamma' and the external convergence kappa_ext; we marginalize over these parameters, assigning well-motivated priors for them, and so turn the major systematic errors into statistical ones. The HST images provide one such prior, constraining the lens mass density profile logarithmic slope to be gamma'=2.08+/-0.03; a combination of numerical simulations and photometric observations of the B1608+656 field provides an estimate of the prior for kappa_ext: 0.10 +0.08/-0.05. This latter distribution dominates the final uncertainty on H_0. Compared with previous work on this system, the new data provide an increase in precision of more than a factor of two. In combination with the WMAP 5-year data set, we find that the B1608+656 data set constrains the curvature parameter to be -0.031 < Omega_k < 0.009 (95% CL), a level of precision comparable to that afforded by the current Type Ia SNe sample. Asserting a flat spatial geometry, we find that, in combination with WMAP, H_0 = 69.7 +4.9/-5.0 km/s/Mpc and w=-0.94 +0.17/-0.19 (68% CL), suggesting that the observations of B1608+656 constrain w as tightly as do the current Baryon Acoustic Oscillation data. (abridged)Comment: 24 pages, 8 figures, revisions based on referee's comments, accepted for publication in Ap

Adaptive Learning Algorithms for Non-stationary Data

With the wide availability of large amounts of data and acute need for extracting useful information from such data, intelligent data analysis has attracted great attention and contributed to solving many practical tasks, ranging from scientific research, industrial process and daily life. In many cases the data evolve over time or change from one domain to another. The non-stationary nature of the data brings a new challenge for many existing learning algorithms, which are based on the stationary assumption. This dissertation addresses three crucial problems towards the effective handling of non-stationary data by investigating systematic methods for sample reweighting. Sample reweighting is a problem that infers sample-dependent weights for a data collection to match another data collection which exhibits distributional difference. It is known as the density-ratio estimation problem and the estimation results can be used in several machine learning tasks. This research proposes a set of methods for distribution matching by developing novel density-ratio methods that incorporate the characters of different non-stationary data analysis tasks. The contributions are summarized below. First, for the domain adaptation of classification problems a novel discriminative density-ratio method is proposed. This approach combines three learning objectives: minimizing generalized risk on the reweighted training data, minimizing class-wise distribution discrepancy and maximizing the separation margin on the test data. To solve the discriminative density-ratio problem, two algorithms are presented on the basis of a block coordinate update optimization scheme. Experiments conducted on different domain adaptation scenarios demonstrate the effectiveness of the proposed algorithms. Second, for detecting novel instances in the test data a locally-adaptive kernel density-ratio method is proposed. While traditional novelty detection algorithms are limited to detect either emerging novel instances which are completely new, or evolving novel instances whose distribution are different from previously-seen ones, the proposed algorithm builds on the success of the idea of using density ratio as a measure of evolving novelty and augments with structural information of each data instance's neighborhood. This makes the estimation of density ratio more reliable, and results in detection of emerging as well as evolving novelties. In addition, the proposed locally-adaptive kernel novelty detection method is applied in the social media analysis and shows favorable performance over other existing approaches. As the time continuity of social media streams, the novelty is usually characterized by the combination of emerging and evolving. One reason is the existence of large common vocabularies between different topics. Another reason is that there are high possibilities of topics being continuously discussed in sequential batch of collections, but showing different level of intensity. Thus, the presented novelty detection algorithm demonstrates its effectiveness in the social media data analysis. Lastly, an auto-tuning method for the non-parametric kernel mean matching estimator is presented. It introduces a new quality measure for evaluating the goodness of distribution matching which reflects the normalized mean square error of estimates. The proposed quality measure does not depend on the learner in the following step and accordingly allows the model selection procedures for importance estimation and prediction model learning to be completely separated