Galaxy Morphology without Classification: Self-organizing Maps

Details Galaxy Morphology without Classification: Self-organizing Maps Galaxy Morphology without Classification: Self-organizing Maps

Aug 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997apjs..111..357n&link_type=abstract

Astrophysical Journal Supplement v.111, p.357

Astronomy and Astrophysics

Astronomy

20

Galaxies: Evolution, Galaxies: Fundamental Parameters, Galaxies: Structure

Scientific paper

We examine a general framework for visualizing data sets of high (greater than 2) dimensionality and demonstrate the framework by taking the morphology of galaxies at moderate redshifts as an example. The distributions of various populations of such galaxies are examined in a space spanned by four purely morphological parameters. Galaxy images are taken from the Hubble Space Telescope Wide Field Planetary Camera 2 in the I band (using the F814W filter). Since we have little prior knowledge on how galaxies are distributed in morphology space, we use an unsupervised learning method (a variant of Kohonen's self-organizing maps, or SOMs). This method allows the data to organize themselves onto a two-dimensional space while conserving most of the topology of the original space. It thus enables us to visualize the distribution of galaxies and study it more easily. The process is fully automated, does not rely on any kind of "eyeball" classification and is readily applicable to large numbers of images. We apply it to a sample of 2934 galaxies and find that the morphology correlates well with the apparent magnitude distribution and, to a lesser extent, with color and bulge dominance. The resulting map traces a morphological sequence similar to the Hubble sequence, albeit two-dimensional. We use the SOM as a diagnostic tool and rediscover a population of bulge-dominated galaxies with morphologies characteristic of peculiar galaxies. This result is achieved without recourse to classification by eye. We also examine the effect of noise on the resulting SOM, and conclude that our results are reliable down to an I magnitude of 24. We propose using this method as a framework into which more physical data can be incorporated as they become available. We hope that this method will lead to a deeper understanding of galaxy evolution.

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