Shape Index Descriptors Applied to Texture-Based Galaxy Analysis

A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical im-age data. A representative sample of images of low-redshift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a testbed. The goal of applying texture descriptors to these data is to extract novel information about galaxies; information which is often lost in more traditional analy-sis. In this study, we build a regression model for predict-ing a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradi-ent orientation histograms as well as multi-scale shape in-dex histograms, which lead to a new descriptor. Our re-sults show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augment-ing the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone. 1