Exploring Methods of Effective Data Display in an Interactive Astronomical Data-processing Environment

1979 ◽  
Vol 49 ◽  
pp. 143-155
Author(s):  
R.J. Allen
Keyword(s):  
Data Processing ◽  
Applied Mathematics ◽  
Radio Sources ◽  
Cosmic Radio ◽  
Data Display ◽  
Astronomical Data ◽  
Strong Motivation ◽  
Restoration Algorithms ◽  
Extended Emission ◽  
Pictorial Form

Many of the image restoration algorithms discussed during these past days work best on fields which are virtually empty except for a few discrete sources. But the sky also has faint regions of extended emission; these regions invariably turn out to be of great astrophysical interest and must be represented as accurately as possible in the maps. The methods chosen to present the data in pictorial form can have an important effect on the speed and efficacity with which the astronomer can extract the useful information. I would be afraid of boring all of you with this discussion of display methods if I did not know that, besides being well versed in subjects of probability, statistics, and applied mathematics (and in some cases philosophies and polemics), you are also astronomers with a strong motivation to sift out of your data the useful information on the physics of cosmic radio sources, and to do it in the most efficient way possible.

Starlet Transform in Astronomical Data Processing

2015 ◽  
pp. 2053-2098 ◽  
Author(s):  
Jean-Luc Starck ◽  
Fionn Murtagh ◽  
Mario Bertero
Keyword(s):  
Data Processing ◽  
Astronomical Data

scholarly journals Synthesis Observations of Southern Radio Sources at 1410 MHz with the Parkes Interferometer. I. Instrument, Reduction, and Preliminary Results

1973 ◽  
Vol 26 (5) ◽  
pp. 661 ◽  
Author(s):  
UJ Schwarz ◽  
DJ Cole ◽  
D Morris
Keyword(s):  
Data Processing ◽  
Computer Control ◽  
Radio Sources ◽  
Preliminary Results ◽  
On Line ◽  
Optical Identifications

Modifications to the Parkes interferometer are described which allow synthesis observations to be made while still retaining the flexibility of frequent baseline changes. Details are given of the receiver with a phase stabilizing device and its performance, on-line computer control, and data processing. Preliminary observations with a resolution of l' of the two sources PKS 2152-69 and 2356-61 and possible optical identifications are discussed briefly.

scholarly journals COSMIC Radio Occultation Processing: Cross-Center Comparison and Validation

2011 ◽  
Vol 28 (6) ◽  
pp. 737-751 ◽  
Author(s):  
Michael E. Gorbunov ◽  
A. V. Shmakov ◽  
Stephen S. Leroy ◽  
Kent B. Lauritsen
Keyword(s):  
Data Processing ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Processing System ◽  
Fourier Integral ◽  
Small Scale ◽  
Cosmic Radio ◽  
Structural Uncertainty ◽  
Height Range ◽  
The Tropics

Abstract A radio occultation data processing system (OCC) was developed for numerical weather prediction and climate benchmarking. The data processing algorithms use the well-established Fourier integral operator–based methods, which ensure a high accuracy of retrievals. The system as a whole, or in its parts, is currently used at the Global Navigation Satellite System Receiver for Atmospheric Sounding (GRAS) Satellite Application Facility at the Danish Meteorological Institute, German Weather Service, and Wegener Center for Climate and Global Change. A statistical comparison of the inversions of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) data by the system herein, University Corporation for Atmospheric Research (UCAR) data products, and ECMWF analyses is presented. Forty days of 2007 and 2008 were processed (from 5 days in the middle of each season) for the comparison of OCC and ECMWF, and 20 days of April 2009 were processed for the comparison of OCC, UCAR, and ECMWF. The OCC and UCAR inversions are consistent. For the tropics, the systematic difference between OCC and UCAR in the retrieved refractivity in the 2–30-km height interval does not exceed 0.1%; in particular, in the 9–25-km interval it does not exceed 0.03%. Below 1 km in the tropics the OCC – UCAR bias reaches 0.2%, which is explained by different cutoff and filtering schemes implemented in the two systems. The structure of the systematic OCC – ECMWF difference below 4 km changes in 2007, 2008, and 2009, which is explained by changes in the ECMWF analyses and assimilation schemes. It is estimated that in the 4–30-km height range the OCC occultation processing system obtains refractivities with a bias not exceeding 0.2%. The random error ranges from 0.3%–0.5% in the upper troposphere–lower stratosphere to about 2% below 4 km. The estimate of the bias below 4 km can currently be done with an accuracy of 0.5%–1% resulting from the structural uncertainty of the radio occultation (RO) data reflecting the insufficient knowledge of the atmospheric small-scale structures and instrumental errors. The OCC – UCAR bias is below the level of the structural uncertainty.

scholarly journals AXS: A Framework for Fast Astronomical Data Processing Based on Apache Spark

2019 ◽  
Vol 158 (1) ◽  
pp. 37 ◽  
Author(s):  
Petar Zečević ◽  
Colin T. Slater ◽  
Mario Jurić ◽  
Andrew J. Connolly ◽  
Sven Lončarić ◽  
...  
Keyword(s):  
Data Processing ◽  
Apache Spark ◽  
Astronomical Data

scholarly journals What will the future of cloud-based astronomical data processing look like?

2016 ◽  
Vol 12 (S325) ◽  
pp. 27-31
Author(s):  
Andrew W. Green ◽  
Elizabeth Mannering ◽  
Lloyd Harischandra ◽  
Minh Vuong ◽  
Simon O’Toole ◽  
...  
Keyword(s):  
Parallel Processing ◽  
Data Processing ◽  
Large Datasets ◽  
Data Archive ◽  
Astronomical Data ◽  
Processing Times ◽  
The Future ◽  
Very Large Datasets ◽  
User Friendly

AbstractAstronomy is rapidly approaching an impasse: very large datasets require remote or cloud-based parallel processing, yet many astronomers still try to download the data and develop serial code locally. Astronomers understand the need for change, but the hurdles remain high. We are developing a data archive designed from the ground up to simplify and encourage cloud-based parallel processing. While the volume of data we host remains modest by some standards, it is still large enough that download and processing times are measured in days and even weeks. We plan to implement a python based, notebook-like interface that automatically parallelises execution. Our goal is to provide an interface sufficiently familiar and user-friendly that it encourages the astronomer to run their analysis on our system in the cloud—astroinformatics as a service. We describe how our system addresses the approaching impasse in astronomy using the SAMI Galaxy Survey as an example.

scholarly journals OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing

2016 ◽  
Vol 129 (972) ◽  
pp. 024001 ◽  
Author(s):  
Shoulin Wei ◽  
Feng Wang ◽  
Hui Deng ◽  
Cuiyin Liu ◽  
Wei Dai ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Data Processing ◽  
Astronomical Data ◽  
Computing Framework
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