Data in astronomy is rapidly growing with upcoming surveys producing 30 TB of images per night. Highly informative spectra are too expensive to measure for each detected object, hence ways of reliably estimating physical properties from images alone are paramount. The objective of this work is to test whether a “big data ready” k-nearest neighbour regression can successfully estimate the specific star formation rate (sSFR) from colours of low-redshift galaxies. The nearest neighbour algorithm achieves a root mean square error (RMSE) of 0.30, outperforming the state-of-the-art astronomical model achieving a RMSE of 0.36.

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 image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results 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 augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.