A simple prescription for simulating and characterizing gravitational arcs

Simple models of gravitational arcs are crucial to simulate large samples of these objects with full control of the input parameters. These models also provide crude and automated estimates of the shape and structure of the arcs, which are necessary when trying to detect and characterize these objects on massive wide area imaging surveys. We here present and explore the ArcEllipse, a simple prescription to create objects with shape similar to gravitational arcs. We also present PaintArcs, which is a code that couples this geometrical form with a brightness distribution and adds the resulting object to images. Finally, we introduce ArcFitting, which is a tool that fits ArcEllipses to images of real gravitational arcs. We validate this fitting technique using simulated arcs and apply it to CFHTLS and HST images of tangential arcs around clusters of galaxies. Our simple ArcEllipse model for the arc, associated to a S\'ersic profile for the source, recovers the total signal in real images typically within 10%-30%. The ArcEllipse+S\'ersic models also automatically recover visual estimates of length-to-width ratios of real arcs. Residual maps between data and model images reveal the incidence of arc substructure. They may thus be used as a diagnostic for arcs formed by the merging of multiple images. The incidence of these substructures is the main factor preventing ArcEllipse models from accurately describing real lensed systems.Comment: 12 pages, 11 figures, accepted for publication in A&

Identificação, coleta, mapeamento e conservação de variedades tradicionais e espécies silvestres de arroz no Brasil.

Implementação de projeto que permitiu a identificação da ocorrência e coleta de acessos de variedades tradicionais e espécies silvestres de arroz em locais ainda não amostrados, complementando as coletas de germoplasma iniciadas há 26 anos no Brasil. Estes acessos foram localizados precisamente com aparelho GPS e estas informações (juntamente com as dos acessos já amostrados nos últimos 26 anos) foram utilizadas para posicioná-los no mapa do Brasil através do software Spring 3.5. Foram obtidos cinco mapas individuais (um para as variedades tradicionais e um para cada uma das espécies silvestres de Oryza), além de um mapa consenso reunindo todos estes mapas. Juntamente com os mapas, foi elaborado um banco de dados contendo as informações obtidas na coleta dos acessos (coordenadas geográficas, altitude, tipo de solo, clima, etc.). A realização de um seminário técnico, ao final do projeto, permitiu à equipe conhecer a extensão dos resultados obtidos, analisar estes resultados, e finalmente, compor o relatório final.bitstream/CNPAF-2009-09/27965/1/doc_220.pd

Identificação, coleta, mapeamento e conservação de variedades tradicionais e espécies silvestres de arroz no Brasil.

Implementação de projeto que permitiu a identificação da ocorrência e coleta de acessos de variedades tradicionais e espécies silvestres de arroz em locais ainda não amostrados, complementando as coletas de germoplasma iniciadas há 26 anos no Brasil. Estes acessos foram localizados precisamente com aparelho GPS e estas informações (juntamente com as dos acessos já amostrados nos últimos 26 anos) foram utilizadas para posicioná-los no mapa do Brasil através do software Spring 3.5. Foram obtidos cinco mapas individuais (um para as variedades tradicionais e um para cada uma das espécies silvestres de Oryza), além de um mapa consenso reunindo todos estes mapas. Juntamente com os mapas, foi elaborado um banco de dados contendo as informações obtidas na coleta dos acessos (coordenadas geográficas, altitude, tipo de solo, clima, etc.). A realização de um seminário técnico, ao final do projeto, permitiu à equipe conhecer a extensão dos resultados obtidos, analisar estes resultados, e finalmente, compor o relatório final.bitstream/CPAF-RR-2009-09/10887/1/doc_220.pd

Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey

We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 2

Target Selection for the Apache Point Observatory Galactic Evolution Experiment (APOGEE)

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) is a high-resolution infrared spectroscopic survey spanning all Galactic environments (i.e., bulge, disk, and halo), with the principal goal of constraining dynamical and chemical evolution models of the Milky Way. APOGEE takes advantage of the reduced effects of extinction at infrared wavelengths to observe the inner Galaxy and bulge at an unprecedented level of detail. The survey's broad spatial and wavelength coverage enables users of APOGEE data to address numerous Galactic structure and stellar populations issues. In this paper we describe the APOGEE targeting scheme and document its various target classes to provide the necessary background and reference information to analyze samples of APOGEE data with awareness of the imposed selection criteria and resulting sample properties. APOGEE's primary sample consists of ~100,000 red giant stars, selected to minimize observational biases in age and metallicity. We present the methodology and considerations that drive the selection of this sample and evaluate the accuracy, efficiency, and caveats of the selection and sampling algorithms. We also describe additional target classes that contribute to the APOGEE sample, including numerous ancillary science programs, and we outline the targeting data that will be included in the public data releases.Comment: Accepted to AJ. 31 pages, 11 figure