Objective The goal is to test whether large-scale machine learning techniques can accurately estimate the specific star formation rate (sSFR) from colours of low-redshift galaxies.
Nearest neighbor density ratio estimation for large-scale applications in astronomy. Natural metrics and least-committed priors for articulated tracking.
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-redshift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a testbed. The goal of applying texture descriptors to these data is to… (More)
Objective The aim is to test whether texture descriptors can extract novel information about physical quantities of galaxies, which normally require spectroscopy to determine. The specific star formation rate (sSFR) is chosen as the quantity in question. Methods We use three sets of features: • Colours from five bandpass filters (no texture information). •… (More)