28,878 research outputs found
The SOAR Gravitational Arc Survey - I: Survey overview and photometric catalogs
We present the first results of the SOAR (Southern Astrophysical Research)
Gravitational Arc Survey (SOGRAS). The survey imaged 47 clusters in two
redshift intervals centered at and , targeting the richest
clusters in each interval. Images were obtained in the , and
bands using the SOAR Optical Imager (SOI), with a median seeing of 0.83, 0.76
and 0.71 arcsec, respectively, in these filters. Most of the survey clusters
are located within the Sloan Digital Sky Survey (SDSS) Stripe 82 region and all
of them are in the SDSS footprint. Photometric calibration was therefore
performed using SDSS stars located in our SOI fields. We reached for galaxies
in all fields the detection limits of , and for a signal-to-noise ratio (S/N) = 3. As a by-product of the image
processing, we generated a source catalogue with 19760 entries, the vast
majority of which are galaxies, where we list their positions, magnitudes and
shape parameters. We compared our galaxy shape measurements to those of local
galaxies and concluded that they were not strongly affected by seeing. From the
catalogue data, we are able to identify a red sequence of galaxies in most
clusters in the lower range. We found 16 gravitational arc candidates
around 8 clusters in our sample. They tend to be bluer than the central
galaxies in the lensing cluster. A preliminary analysis indicates that of the clusters have arcs around them, with a possible indication of a
larger efficiency associated to the high- systems when compared to the
low- ones. Deeper follow-up images with Gemini strengthen the case for the
strong lensing nature of the candidates found in this survey.Comment: 17 pages, 11 figures (most of them multi-panel) MNRAS (2013
Multi-color detection of gravitational arcs
Strong gravitational lensing provides fundamental insights into the
understanding of the dark matter distribution in massive galaxies, galaxy
clusters and the background cosmology. Despite their importance, the number of
gravitational arcs discovered so far is small. The urge for more complete,
large samples and unbiased methods of selecting candidates is rising. A number
of methods for the automatic detection of arcs have been proposed in the
literature, but large amounts of spurious detections retrieved by these methods
forces observers to visually inspect thousands of candidates per square degree
in order to clean the samples. This approach is largely subjective and requires
a huge amount of eye-ball checking, especially considering the actual and
upcoming wide field surveys, which will cover thousands of square degrees. In
this paper we study the statistical properties of colours of gravitational arcs
detected in the 37 deg^2 of the CARS survey. We have found that most of them
lie in a relatively small region of the (g'-r',r'-i') colour-colour diagram. To
explain this property, we provide a model which includes the lensing optical
depth expected in a LCDM cosmology that, in combination with the sources'
redshift distribution of a given survey, in our case CARS, peaks for sources at
redshift z~1. By further modelling the colours derived from the SED of the
galaxies dominating the population at that redshift, the model well reproduces
the observed colours. By taking advantage of the colour selection suggested by
both data and model, we show that this multi-band filtering returns a sample
83% complete and a contamination reduced by a factor of ~6.5 with respect to
the single-band arcfinder sample. New arc candidates are also proposed.Comment: 13 pages, 7 figures, 4 tables; title modified, text extended, figures
improved, error estimate improve
A vision-based system for inspecting painted slates
Purpose – This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates.
Design/methodology/approach – The vision system that has been developed consists of two major components covering the opto-mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface.
Findings – The inspection system was developed on 400 slates to determine the threshold settings that give the best trade-off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples.
Practical implications – The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory-type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system.
Originality/value – The development of a real-time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non-uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set-up and the development of multi-component image-processing inspection algorithm
Visual detection of blemishes in potatoes using minimalist boosted classifiers
This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image.
A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted.
Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes.
With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc.
The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost.
In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively
Index to NASA Tech Briefs, January - June 1966
Index to NASA technological innovations for January-June 196
SlicerAstro: a 3-D interactive visual analytics tool for HI data
SKA precursors are capable of detecting hundreds of galaxies in HI in a
single 12 hours pointing. In deeper surveys one will probe more easily faint HI
structures, typically located in the vicinity of galaxies, such as tails,
filaments, and extraplanar gas. The importance of interactive visualization has
proven to be fundamental for the exploration of such data as it helps users to
receive immediate feedback when manipulating the data. We have developed
SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on
traditional 2-D input/output hardware. These capabilities enhance the data
inspection, allowing faster analysis of complex sources than with traditional
tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform
open source software package for visualization and medical image processing.
We demonstrate the capabilities of the current stable binary release of
SlicerAstro, which offers the following features: i) handling of FITS files and
astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii)
interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D
modeling. In addition, SlicerAstro has been designed with a strong, stable and
modular C++ core, and its classes are also accessible via Python scripting,
allowing great flexibility for user-customized visualization and analysis
tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing.
SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr
Survey for Emission-Line Galaxies: Universidad Complutense de Madrid List 3
A new low-dispersion objective-prism search for low-redshift (z<0.045)
emission-line galaxies (ELG) has been carried out by the Universidad
Complutense de Madrid with the Schmidt Telescope at the Calar-Alto Observatory.
This is a continuation of the UCM Survey, which was performed by visual
selection of candidates in photographic plates via the presence of the
Halpha+[NII]6584 blend in emission. In this new list we have applied an
automatic procedure, fully developed by us, for selecting and analyzing the ELG
candidates on the digitized images obtained with the MAMA machine. The analyzed
region of the sky covers 189 square degrees in nine fields near R.A.=14h & 17h,
Dec=25 deg. The final sample contains 113 candidates. Special effort has been
made to obtain a large amount of information directly from our uncalibrated
plates by using several external calibrations. The parameters obtained for the
ELG candidates allow for the study of the statistical properties for the
sample.Comment: 13 pages, 18 PostScript figures, 6 JPEG figures, Table 2 corrected.
Accepted for publication in Astrophysical Journal Supplements, also available
at http://www.ucm.es/info/Astrof/opera/LIST3_ApJS99
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