Observational High Time Resolution Astrophysics differs from conventional astrophysics in regard to the detectors
employed which have a time resolution less than that obtainable through CCD with a normal readout τ < a few
minutes. This paper looks at the implications for HTRA from extremely large telescopes and specifically, as an
exemplar its possible impact on pulsar astrophysics. We demonstrate, by using the derived point-spread-function
from models of the Euro50 telescope, the possible effects active and adaptive mirrors have on observing rapidly
varying astronomical objects.
KEYWORDS: Device simulation, Image processing, Mining, Telescopes, Image filtering, Data archive systems, Observatories, Signal to noise ratio, Astronomy, Data mining
As the astronomical community continues to produce deeper and higher resolution data, it becomes increasingly important to provide tools to the scientist that help mining the data in order to provide only the scientifically interesting images. In the case of uncalibrated archives, this task is especially difficult as it is difficult to know whether an interesting source can be seen on images without actually looking. Here, we show how instrument simulation can be used to lightly process the database-stored image descriptors of the ESO/Wide Field Imager (WFI) archive, and compute the corresponding limiting magnitudes. The end result is a more scientific description of the ESO/ST-ECF archive contents, allowing a more astronomer-friendly archive user interface, and hence increasing the archive useability in the context of a Virtual Observatory. This method is developed for improving the Querator search engine of ESO/HST archive, in the context of the EC funded ASTROVIRTEL project, but also provides an independant tool that can be adapted to other archives.
To take advantage of the recent upsurge in astrophysical research applications of grid technologies coupled with the increase in temporal and spatial coverage afforded to us by dedicated all-sky surveys and on-line data archives, we have developed an automated image reduction and analysis pipeline for a number of different astronomical instruments. The primary science goal of the project is in the study of long-term optical variability of brown dwarfs, although it can be tailored to suit many varied astrophysical phenomena. The pipeline complements Querator, the custom search-engine which accesses the astronomical image archives based at the ST-ECF/ESO centre in Garching, Germany. To increase our dataset we complement the reduction and analysis of WFI (Wide Field Imager, mounted on the 2.2-m MPG/ESO telescope at La Silla) archival images with the analysis of pre-reduced co-spatial HST/WFPC2 images and near infrared images from the DENIS archive. Our pipeline includes CCD-image reduction, registration, astrometry, photometry, and image matching stages. We present sample results of all stages of the pipeline and describe how we overcome such problems as missing or incorrect image meta-data, interference fringing, poor image calibration files etc. The pipeline was written using tasks contained in the IRAF environment, linked together with Unix Shell Scripts and Perl, and the image reduction and analysis is performed using a 40-processor Origin SGI 3800 based at NUI, Galway.
In this paper, we deal with FOCA ultraviolet data and their cross-referencing with the DPOSS optical catalog, through data mining techniques. While traditional cross-referencing consists in correcting catalog coordinates in order to seek nearest candidate, non-optical surveys tend to have lower resolutions and more coordinates uncertainties. Then, it seemed to be a loss not to use more light sources parameters obtained through image processing pipelines. A data mining approach based on decision trees (machine learning algorithms), we processed different FOCA/DPOSS sources pairs that we could suppose being the same stellar entity, and some other pairs, obviously too distant to match. Trees use every existing ultraviolet/optical parameter present on catalog, excluding only coordinates. The resulting trees allows a classification of any FOCA/DPOSS pair, giving a probability for the pair to match, i.e. come from the same source. The originality of this method is the use of non-position parameters, that can be used for cross-referencing various catalogs in different wavelength without the need to homogenize coordinates systems. Such methods could be tools for working on upcoming multi-wavelength catalogs.
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