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The feasibility of using neural networks to recognize morphological features of galaxies is examined. Neural networks are an artificial intelligence technique which simulate groups of biological neurons and their interconnections to utilize their ability to learn and generalize. The networks are trained with a set of galaxies of known morphology imaged in the O and E bandpasses from the region of the North Galactic Pole. The galaxy images were digitized by the Minnesota Automated Plate Scanner from nine fields of the first epoch Palomar Observatory Sky Survey. Once trained, the networks are tested with an independent set of galaxies from the same field to assess their ability to recognize features such as spiral structure and bars. The use of image data as direct inputs to the neural networks is compared to that of using photometric properties derived from the images.
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