Automated galaxy classification in large sky surveys

Current efforts to perform automatic galaxy classification using artificial neural network image classifiers are reviewed. For both digitized photographic Schmidt plate data and newly obtained WEPC2 images from the Hubble space telescope, a variety of 2D photometric parameter spaces produce a segregation of galaxy Hubble types. Through the use of hidden node layers, a neural network is capable of mapping complicated, highly nonlinear data spaces. This powerful technique is used to map multivariate photometric parameter spaces to the revised Hubble system of galaxy classification. A promising new approach using neural network analysis of Fourier image models is discussed in the context of morphological bar detection