Automated Galaxy Morphology: A Fourier Approach

Abstract

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in concentric elliptical annuli centered on the galaxy. Both the phase and amplitude of each Fourier component have been studied as a function of radial bin number for a large collection of galaxy images using principal component analysis. We find that up to 90 percent of the variance in many of these Fourier profiles may be characterized in as few as 3 principal components and their use substantially reduces the dimensionality of the classification problem. We use supervised learning methods in the form of artificial neural networks to train galaxy classifiers that detect morphological bars at the 85-90 percent confidence level and can identify the Hubble type with a 1-sigma scatter of 1.5 steps on the 16-step stage axis of the revised Hubble system. Finally, we systematically characterize the adverse effects of decreasing resolution and S/N on the quality of morphological information predicted by these classifiers.