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We examine a general framework for visualizing datasets of high (> 2)dimensionality, and demonstrate it using the morphology of galaxies at moderateredshifts. The distributions of various populations of such galaxies areexamined in a space spanned by four purely morphological parameters. Galaxyimages are taken from the Hubble Space Telescope (HST) Wide Field PlanetaryCamera 2 (WFPC2) in the I band (F814W). Since we have little prior knowledge onhow galaxies are distributed in morphology space we use an unsupervisedlearning method (a variant of Kohonen's Self Organizing Maps, or SOMs). Thismethod allows the data to organize themselves onto a two-dimensional spacewhile conserving most of the topology of the original space. It thus enables usto visualize the distribution of galaxies and study it more easily. The processis fully automated, does not rely on any kind of eyeball classification and isreadily applicable to large numbers of images. We apply it to a sample of 2934galaxies, and find that morphology correlates well with the apparent magnitudedistribution and to lesser extents with color and bulge dominance. Theresulting map traces a morphological sequence similar to the Hubble Sequence,albeit two dimensional. We use the SOM as a diagnostic tool, and rediscover apopulation of bulge-dominated galaxies with morphologies characteristic ofpeculiar galaxies. This is achieved without recourse to eyeball classification.We also examine the effect of noise on the resulting SOM, and conclude thatdown to I magnitude of 24 our results are reliable. We propose using thismethod as a framework into which more physical data can be incorporated whenthey become available. Hopefully, this will lead to a deeper understanding ofgalaxy evolution.

Galaxy Morphology without Classification: Self‐organizing Maps