Multi-color classification in the calar alto deep imaging survey

We use a multi-color classification method introduced by Wolf et al. (2001) to reliably identify stars, galaxies and quasars in the up to 16-dimension al color space: provided by the filter set of the Calar Alto Deep Imaging S urvey (CADIS). The samples of stars, galaxies and quasars obtained this way have been used for dedicated studies which are published in separate paper s. The classification is good enough to detect quasars rather completely an d efficiently without confirmative spectroscopy. The multi-color redshifts are accurate enough for most statistical applications, e.g. evolutionary st udies of the galaxy luminosity function. Also, the separation between stars and galaxies reaches deeper than with morphological criteria, so that stud ies of the stellar population can be extended to fainter levels. We charact erize the dataset presently available on the CADIS 1 h-, 9 h- and 16 h-fiel ds. Using Monte-Carlo simulations we model the classification performance e xpected for CADIS. We present a summary of the classification results on th e CADIS database and discuss unclassified objects. More than 99% of the who le catalog sample at R < 22 (more than 95% at R < 23) are successfully clas sified matching the expectations derived from the simulations. A small numb er of peculiar objects challenging the classification is discussed in detai l. Spectroscopic observations are used to check the reliability of the mult i-color classification (6 mistakes among 151 objects with R < 24). From the se, we also determine the accuracy of the multi-color redshifts which are r ather good for galaxies (<sigma>(z) approximate to 0.03) and useful for qua sars. We find that the classification performance derived from the simulati ons compares well with results from the real survey. Finally, we locate are as for potential improvement of the classification.