next previous
Up: An image database


1 Introduction

For several years we have been embarked on automatic galaxy extraction from various sources of images. Indeed, we are entering a new era, where the visual analysis will be replaced by an automatic one. MacGillivray et al. (1987) initiated automatic galaxy recognition with the COSMOS machine. A few years later similar techniques were used by Maddox et al. (1990) for the construction of the APM catalogue. Independently, Lauberts and Valentijn applied automatic surface photometry on galaxies discovered from a visual inspection. Nevertheless, we are still at the beginning of this process and new tools have to be invented. Some of the techniques used here were not even imaginable a few years ago.

Our general purpose is twofold:

In a preliminary study (Paturel et al. 1996) our target was limited to identification of galaxies already known in the LEDA[*] database and to extraction of some astrophysical parameters using our own digitization of the Palomar Sky Survey. The resolution of the digitization was too poor (6 $^{\prime\prime}$) to allow recognition of new galaxies. We developed source extraction and automatic cross-identification algorithms.

In a second study (Vauglin et al. 1998) the same source extraction algorithms were used but we applied an automatic galaxy recognition based on Discriminant Analysis method. The I-band CCD images were obtained with the 1-meter ESO telescope for the Deep Near Infrared Survey (DENIS). Because of the high dynamic of the CCD receiver the separation between stars and galaxies is relatively easy. Stars have a very high central intensity and a small surface area while galaxies do not.

In the present study we are aiming at the most difficult task of recognizing new galaxies from the digitization of photographic plates (POSS1 and UK Schmidt plates). Because of plate properties the center of a source (star or galaxy) is generally saturated and only a few pieces of information can be derived from optical density of pixels[*]. Besides, the material is made of very inhomogeneous plates taken from very different regions. It is simply not possible to imagine that the same method will work in all conditions. This major conclusion of our preliminary analysis will force us to imagine a method adapted to each individual plate.

The different methods of automatic recognition can presently be classified in 5 classes as follows:

The main characteristic of these methods is that they require a proper choice of parameters describing each object. This choice is not obvious. It is guided by the results obtained on a training sample for which each object has been classified by an expert. This is the main difficulty in the present application. The material (see Sect. 2) is so inhomogeneous that it would be necessary to build a training sample for each plate. This would mean classifying about $500\ 000$ objects by eye. So, we worked in another way. In Sect. 3 we show that the diagram of the dispersion of pixel optical densities (i.e. standard error of the pixel intensities) versus the inverse of the surface area of a given object performs this Star/Galaxy separation well. Thus, we plotted these diagrams for each plate and made the separation between stars and galaxies by adopting a frontier function in an interactive manner. This constitutes the first step leading us to a preliminary catalogue of 4.3 million galaxy candidates and 47.4 million star candidates. In Sect. 4, we built a large, general training sample of $258\ 983$ stars and $87\ 725$ galaxies by cross-identifying our preliminary catalog with well established star and galaxy catalogs. This training sample was used to setup a neural network allowing us to filter the star/galaxy candidates. After this filtering step we got an all-sky catalogue of 3.2 million galaxy candidates. Finally, in Sect. 5 we made the internal cross-identification (what we call the Auto-crossidentification) for galaxies seen several times on different plates. Then, we made the cross-identification with LEDA galaxies and cleaned the catalogue in order to remove contamination by known extended objects (Planetary Nebulae, Globular clusters, Open clusters, Bright Nebulae, Bright Stars) and by very faint galactic stars. This led to the final catalogue of $2\ 772\ 061$ galaxy candidates.


next previous
Up: An image database

Copyright The European Southern Observatory (ESO)