Automated Galaxy Morphology: A Fourier Approach

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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% of the variance in many of these Fourier profiles may be characterized in as few as three principal components and that 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% confidence level and can identify the Hubble type with a 1 σ 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 signal-to-noise ratio on the quality of morphological information predicted by these classifiers.