FOCAS AUTOMATIC CATALOG MATCHING ALGORITHMS

This article describes efficient algorithms that automatically take tw o or more catalogs of objects with instrumental coordinates and magnit udes and matches them. The challenges are that the instrumental coordi nates may be only partially overlapping, at a different scale, rotated , or even inverted (flipped). The object magnitudes may be derived fro m different passbands so that the relative magnitudes of the objects d iffer. Also, the catalog may not contain all the same objects due to d ifferences in separating close objects or to partial overlap between i mages. Finally, the catalog positions and magnitudes are subject to no ise in the images from which they were derived. The algorithms are app licable to any automated cataloging system. However, the implementatio n described here is part of the faint-object classification and analys is system (FOCAS). FOCAS automatically produces catalogs of objects fr om digital images. The algorithms described here first take a subsampl e of the brightest objects from the catalogs and find a coordinate tra nsformation between one catalog, the reference catalog, and other cata logs. Then all the objects in the catalogs are matched based on the tr ansformed reference coordinates.