A Selection of Giant Radio Sources from NVSS
  Results of the application of pattern recognition techniques to the problem
of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are
presented and issues affecting the process are explored. Decision-tree pattern
recognition software was applied to training set source pairs developed from
known NVSS large angular size radio galaxies. The full training set consisted
of 51,195 source pairs, 48 of which were known GRS for which each lobe was
primarily represented by a single catalog component. The source pairs had a
maximum separation of 20 arc minutes and a minimum component area of 1.87
square arc minutes at the 1.4 mJy level. The importance of comparing resulting
probability distributions of the training and application sets for cases of
unknown class ratio is demonstrated. The probability of correctly ranking a
randomly selected (GRS, non-GRS) pair from the best of the tested classifiers
was determined to be 97.8 +/- 1.5%. The best classifiers were applied to the
over 870,000 candidate pairs from the entire catalog. Images of higher ranked
sources were visually screened and a table of over sixteen hundred candidates,
including morphological annotation, is presented. These systems include doubles
and triples, Wide-Angle Tail (WAT) and Narrow-Angle Tail (NAT), S- or Z-shaped
systems, and core-jets and resolved cores. While some resolved lobe systems are
recovered with this technique, generally it is expected that such systems would
require a different approach.
