Project EFIGI: Automatic Classification of Galaxies
Résumé
We propose an automatic system to classify images of galaxies with varying resolution. Morphologically typing galaxies is a difficult task in particular for distant galaxies convolved by a point-spread function and suffering from a poor signal-to-noise ratio. In the context of the first phase of the project EFIGI (extraction of the idealized shapes of galaxies in imagery), we present the three steps of our software: cleaning, dimensionality reduction and supervised learning. We present preliminary results derived from a subset of 774 galaxies from the Principal Galaxies Catalog and compare them to human classifications made by astronomers. We use g-band images from the Sloan Digital Sky Survey. Finally, we discuss future improvements which we intend to implement before releasing our tool to the community.