Combining Gaia Windows: The Method and Simulations

Details Combining Gaia Windows: The Method and Simulations Combining Gaia Windows: The Method and Simulations

Aug 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005ap%26ss.298..559n&link_type=abstract

Astrophysics and Space Science, Volume 298, Issue 4, pp.559-575

Astronomy and Astrophysics

Astrophysics

2

Methods, Data Analysis &Ndash, Techniques, Image Processing &Ndash, Binaries, Visual

Scientific paper

During the 5-year-observational phase of Gaia several one-dimensional observations will be obtained from the same objects at different epochs and transit angles. Combining the information from all the transits to produce a single two-dimensional image is scientifically important as this will allow to detect much fainter stars in the vicinity of the main source than otherwise possible from single observations on-board the satellite or from individual transmitted patches. Most of the emerging objects will be physical companions, but some background/foreground objects will also be found. The combination procedure further increases the image resolution in the across scan-direction. The procedure consists of combining patches that are stretched back to 2-D windows, only possible since the centroid positions of the windows are known. The initial image window from which the patches originate is not square, but elongated along the scan-direction if the Astro AF11 field of view is used in the current calculations. Transit angles can take any value between 0∘ and 360∘. The window combining method is a simple mapping procedure that is fast and should handle all realistic complexities occurring during the Gaia data reduction. The method is not intended to be a perfect image restoration technique, but its purpose is to fulfill the scientific requirement of detecting faint neighboring sources existing in the close vicinity of the main targets. In this study we show examples of the combined windows and illustrate how faint companions around primary targets could be detected. We investigate the performances and limitations of the method and discuss possible complexities that might occur during the final data analysis.

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