Zooming into the Cosmic Horseshoe: new insights on the lens profile and the source shape

The gravitational lens SDSS J1148+1930, also known as the Cosmic Horseshoe, is one of the biggest and of the most detailed Einstein rings ever observed. We use the forward reconstruction method implemented in the lens fitting code Lensed to investigate with great detail the properties of the lens and of the background source. We model the lens with different mass distributions, focusing in particular on the determination of the slope of the dark matter component. The inherent degeneracy between the lens slope and the source size can be broken when we can isolate separate components of each lensed image, as in this case. For an elliptical power law model, $\kappa(r) \sim r^{-t}$, the results favour a flatter-than-isothermal slope with a maximum-likelihood value t = 0.08. Instead, when we consider the contribution of the baryonic matter separately, the maximum-likelihood value of the slope of the dark matter component is t = 0.31 or t = 0.44, depending on the assumed Initial Mass Function. We discuss the origin of this result by analysing in detail how the images and the sources change when the slope t changes. We also demonstrate that these slope values at the Einstein radius are not inconsistent with recent forecast from the theory of structure formation in the LambdaCDM model.Comment: 13 pages, 9 figures, accepted for publication in MNRA

Lensed: a code for the forward reconstruction of lenses and sources from strong lensing observations

Robust modelling of strong lensing systems is fundamental to exploit the information they contain about the distribution of matter in galaxies and clusters. In this work, we present Lensed, a new code which performs forward parametric modelling of strong lenses. Lensed takes advantage of a massively parallel ray-tracing kernel to perform the necessary calculations on a modern graphics processing unit (GPU). This makes the precise rendering of the background lensed sources much faster, and allows the simultaneous optimisation of tens of parameters for the selected model. With a single run, the code is able to obtain the full posterior probability distribution for the lens light, the mass distribution and the background source at the same time. Lensed is first tested on mock images which reproduce realistic space-based observations of lensing systems. In this way, we show that it is able to recover unbiased estimates of the lens parameters, even when the sources do not follow exactly the assumed model. Then, we apply it to a subsample of the SLACS lenses, in order to demonstrate its use on real data. The results generally agree with the literature, and highlight the flexibility and robustness of the algorithm.Comment: v2: major revision; accepted by MNRAS; lens reconstruction code available at http://glenco.github.io/lensed

A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It is based on a temporal autoregressive convolution with spatially colored and temporally white innovations. By linking the advection parameter of the convolution kernel to an external wind vector, the model is temporally nonstationary. Further, it allows for nonseparable and anisotropic covariance structures. With the help of the Voronoi tessellation, we construct a natural parametrization, that is, space as well as time resolution consistent, for data lying on irregular grid points. In the application, the statistical model combines forecasts of three other meteorological variables obtained from a numerical weather prediction model with past precipitation observations. The model is then used to predict three-hourly precipitation over 24 hours. It performs better than a separable, stationary and isotropic version, and it performs comparably to a deterministic numerical weather prediction model for precipitation and has the advantage that it quantifies prediction uncertainty.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS564 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Non-perturbative effective model for the Higgs sector of the Standard Model

A non-perturbative effective model is derived for the Higgs sector of the standard model, described by a simple scalar theory. The renormalized couplings are determined by the derivatives of the Gaussian Effective Potential that are known to be the sum of infinite bubble graphs contributing to the vertex functions. A good agreement has been found with strong coupling lattice simulations when a comparison can be made

Strategic Alliances in the U.S. Beef Supply Chain

This study analyzes vertical-coordination practices in the U.S. beef supply chain focusing on strategic alliances. We present results from a survey of beef alliances describing their organizational structure, the nature of participantsÂ’' involvement, contractual requirements, information-sharing practices, services offered to alliance participants, and marketing strategies. Survey results provide a detailed description of 13 beef alliances and are intended to inform potential participants about vertical-coordination alternatives. In addition, the study provides relevant information for future economic research on the formation, organization, and functioning of beef alliances.Livestock Production/Industries, Marketing,