973 research outputs found

    Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking

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    Montage is a portable software toolkit for constructing custom, science-grade mosaics by composing multiple astronomical images. The mosaics constructed by Montage preserve the astrometry (position) and photometry (intensity) of the sources in the input images. The mosaic to be constructed is specified by the user in terms of a set of parameters, including dataset and wavelength to be used, location and size on the sky, coordinate system and projection, and spatial sampling rate. Many astronomical datasets are massive, and are stored in distributed archives that are, in most cases, remote with respect to the available computational resources. Montage can be run on both single- and multi-processor computers, including clusters and grids. Standard grid tools are used to run Montage in the case where the data or computers used to construct a mosaic are located remotely on the Internet. This paper describes the architecture, algorithms, and usage of Montage as both a software toolkit and as a grid portal. Timing results are provided to show how Montage performance scales with number of processors on a cluster computer. In addition, we compare the performance of two methods of running Montage in parallel on a grid.Comment: 16 pages, 11 figure

    Ultra-deep Large Binocular Camera U-band Imaging of the GOODS-North Field: Depth vs. Resolution

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    We present a study of the trade-off between depth and resolution using a large number of U-band imaging observations in the GOODS-North field (Giavalisco et al. 2004) from the Large Binocular Camera (LBC) on the Large Binocular Telescope (LBT). Having acquired over 30 hours of data (315 images with 5-6 mins exposures), we generated multiple image mosaics, starting with the best atmospheric seeing images (FWHM ≲\lesssim0.8"), which constitute ∼\sim10% of the total data set. For subsequent mosaics, we added in data with larger seeing values until the final, deepest mosaic included all images with FWHM ≲\lesssim1.8" (∼\sim94% of the total data set). From the mosaics, we made object catalogs to compare the optimal-resolution, yet shallower image to the lower-resolution but deeper image. We show that the number counts for both images are ∼\sim90% complete to UABU_{AB} ≲26\lesssim26. Fainter than UABU_{AB}∼\sim 27, the object counts from the optimal-resolution image start to drop-off dramatically (90% between UABU_{AB} = 27 and 28 mag), while the deepest image with better surface-brightness sensitivity (μUAB\mu^{AB}_{U}≲\lesssim 32 mag arcsec−2^{-2}) show a more gradual drop (10% between UABU_{AB} ≃\simeq 27 and 28 mag). For the brightest galaxies within the GOODS-N field, structure and clumpy features within the galaxies are more prominent in the optimal-resolution image compared to the deeper mosaics. Finally, we find - for 220 brighter galaxies with UABU_{AB}≲\lesssim 24 mag - only marginal differences in total flux between the optimal-resolution and lower-resolution light-profiles to μUAB\mu^{AB}_{U}≲\lesssim 32 mag arcsec−2^{-2}. In only 10% of the cases are the total-flux differences larger than 0.5 mag. This helps constrain how much flux can be missed from galaxy outskirts, which is important for studies of the Extragalactic Background Light.Comment: 24 pages, 14 figures, submitted to PASP, comments welcom

    Are tiled display walls needed for astronomy?

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    Clustering commodity displays into a Tiled Display Wall (TDW) provides a cost-effective way to create an extremely high resolution display, capable of approaching the image sizes now gen- erated by modern astronomical instruments. Astronomers face the challenge of inspecting single large images, many similar images simultaneously, and heterogeneous but related content. Many research institutions have constructed TDWs on the basis that they will improve the scientific outcomes of astronomical imagery. We test this concept by presenting sample images to astronomers and non- astronomers using a standard desktop display (SDD) and a TDW. These samples include standard English words, wide field galaxy surveys and nebulae mosaics from the Hubble telescope. These experiments show that TDWs provide a better environment for searching for small targets in large images than SDDs. It also shows that astronomers tend to be better at searching images for targets than non-astronomers, both groups are generally better when employing physical navigation as opposed to virtual navigation, and that the combination of two non-astronomers using a TDW rivals the experience of a single astronomer. However, there is also a large distribution in aptitude amongst the participants and the nature of the content also plays a significant role is success.Comment: 19 pages, 15 figures, accepted for publication in PASA (Publications of the Astronomical Society of Australia

    Software Estimates Costs of Testing Rocket Engines

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    Simulation-Based Cost Model (SiCM), a discrete event simulation developed in Extend , simulates pertinent aspects of the testing of rocket propulsion test articles for the purpose of estimating the costs of such testing during time intervals specified by its users. A user enters input data for control of simulations; information on the nature of, and activity in, a given testing project; and information on resources. Simulation objects are created on the basis of this input. Costs of the engineering-design, construction, and testing phases of a given project are estimated from numbers and labor rates of engineers and technicians employed in each phase, the duration of each phase; costs of materials used in each phase; and, for the testing phase, the rate of maintenance of the testing facility. The three main outputs of SiCM are (1) a curve, updated at each iteration of the simulation, that shows overall expenditures vs. time during the interval specified by the user; (2) a histogram of the total costs from all iterations of the simulation; and (3) table displaying means and variances of cumulative costs for each phase from all iterations. Other outputs include spending curves for each phase

    Immersive and Collaborative Data Visualization Using Virtual Reality Platforms

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    Effective data visualization is a key part of the discovery process in the era of big data. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multi dimensional information poses some deep questions in the field of cognition technology and human computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional desktop visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.Comment: 6 pages, refereed proceedings of 2014 IEEE International Conference on Big Data, page 609, ISBN 978-1-4799-5665-

    Spitzer infrared spectrometer 16μm observations of the GOODS fields

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    We present Spitzer 16μm imaging of the Great Observatories Origins Deep Survey (GOODS) fields. We survey 150 arcmin^2 in each of the two GOODS fields (North and South), to an average 3σ depth of 40 and 65 μJy, respectively. We detect ~1300 sources in both fields combined. We validate the photometry using the 3–24μm spectral energy distribution of stars in the fields compared to Spitzer spectroscopic templates. Comparison with ISOCAM and AKARI observations in the same fields shows reasonable agreement, though the uncertainties are large. We provide a catalog of photometry, with sources cross-correlated with available Spitzer, Chandra, and Hubble Space Telescope data. Galaxy number counts show good agreement with previous results from ISOCAM and AKARI with improved uncertainties. We examine the 16–24μm flux ratio and find that for most sources it lies within the expected locus for starbursts and infrared luminous galaxies. A color cut of S_(16)/S_(24) > 1.4 selects mostly sources which lie at 1.1 < z < 1.6, where the 24μm passband contains both the redshifted 9.7 μm silicate absorption and the minimum between polycyclic aromatic hydrocarbon emission peaks. We measure the integrated galaxy light of 16μm sources and find a lower limit on the galaxy contribution to the extragalactic background light at this wavelength to be 2.2 ± 0.2 nW m^(−2) sr^(−1)
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