Neural computation as a tool for galaxy classification: methods and examples

We apply and compare various artificial neural network (ANN) and other algorithms for the automated morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in astronomy. The methods are illustrated using a selection of subsets from the ESO-LV catalogue, for which both machine parameters and human classifications are available. The main methods we explore are: (i) principal component analysis (PCA), which provides information on how independent and informative the input parameters are; (ii) encoder neural networks, which allow us to find both linear (PCA-like) and non-linear combinations of the input, illustrating an example of an unsupervised ANN; and (iii) supervised ANNs (using the backpropagation or quasi-Newton algorithm) based on a training set for which the human classification is known. Here the output for previously unclassified galaxies can be interpreted as either a continuous (analogue) output (for example T-type) or a Bayesian a posteriori probability for each class. Although the ESO-LV parameters are suboptimal, the success of the ANN in reproducing the human classification is 2 T-type units, similar to the degree of agreement between two human experts who classify the same galaxy images on plate material. We also examine the aspects of ANN configurations, reproducibility, scaling of input parameters and redshift information.