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6 Conclusion

After many visual inspections it appears that the method is very efficient for detecting faint galaxies and for determining their proper size and orientation: low surface brightness galaxies are well detected; near the galactic plane many galaxies seen by the eye have been automatically identified and many new galaxies have been discovered. This constitutes an incredible improvement even if some star-like galaxies are missed and if some artefacts remain.

The catalogue has been loaded into the LEDA database. It considerably changes the management of our database because in a given field all galaxies brighter than $18^{\rm th}$ mag are present at their right place. A code has been assigned to each galaxy to measure the reliability of the detection. Progressively, the database will be cleaned for remaining artefacts. Presently, 1 million galaxies can be accessed through LEDA and the corresponding catalogue is available electronically.

We will use the catalogue itself with the matrices of galaxies for several astrophysical purposes:

As a by-product we got a catalogue of 50 millions stars. It is to be noted that among these stars there are compact extragalactic objects. Some of them will be recovered from special analysis in preparation.

Acknowledgements

This work was based on photographic data obtained using The UK Schmidt Telescope. The UK Schmidt Telescope was operated by the Royal Observatory Edinburgh, with funding from the UK Science and Engineering Research Council, until 1988 June, and thereafter by the Anglo-Australian Observatory. Original plate material is copyright (c) the Royal Observatory Edinburgh and the Anglo-Australian Observatory. The plates were processed into the present compressed digital form with their permission. The Digitized Sky Survey was produced at the Space Telescope Science Institute under US Government grant NAG W-2166. The amount of calculation needed by the present study was so large that we were obliged of borrowing many computers during hollidays. We thank Ph. Prugniel, F. Simien, E. Pecontal, E. Emsellem, H. Di Nella, L. Copin for lending their computers. We also thank the Pole Scientifique de Modélisation Numerique (PSMN) for giving us a privilagiate access to their computing facilities. We thank Pierre Valvin, Kristine Bonnefoy and Christelle Lamy-Charrier for helping us to write a neural network program. We also thank Isabelle Frechet for contributing to visual tests of the method and Mikko Hanski for contributing in the large scale analysis.


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