Automated galaxy classification using artificial neural networks

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 WFPC2 imagery from the Hubble Space Telescope, a variety of two-dimensional photometric parameter spaces produce a segregation of Hubble types. Through the use of hidden node layers, a neural network is capable of mapping complicated, highly nonlinear data space. This powerful technique is used to map a multivariate photometric parameter space to the revised Hubble system of galaxy classification.