Computer Science – Performance
Scientific paper
Jan 1999
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999vcud.book.....w&link_type=abstract
Vielfarben-Klassifikation in CADIS und die Suche nach Quasaren., by Wolf, C.. Heidelberg Univ. (Germany). Naturwissenschaftlich
Computer Science
Performance
Scientific paper
The Calar Alto Deep Imaging Survey (CADIS) is a deep photometric multicolor survey on ten rather empty fields of the sky well suited for extragalactic studies. As part of this work, a photometric classification technique was developed which sorts all objects into the classes "star", "galaxy", "quasar" and "unclassified". This classification is based on published spectral libraries. It also performs a redshift estimation for galaxies and quasars. Instead of a template fitting procedure, a mathematically almost equivalent procedure is used in this work that is essentially deduced from classification theory and can be implemented much more efficiently than a template fitting algorithm. A general introduction into the subject of classification is given in this report and its application to CADIS is detailed. The performance of the classification was checked by taking slit spectra of about 100 objects in the CADIS 9 h- and 16 h-fields using MOSCA at Calar Alto. Another 150 identifications in the 3 h-field were taken from the Canada-France Redshift Survey, increasing the number of objects with both photometric and spectroscopic classification to about 250. Among these, only five objects were misclassified (=2%) showing the classification to be excellent. After sorting the CADIS objects into the various classes, the sample of quasar candidates was analysed in more detail.
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