Moreover, automated methods are required to construct the databases themselves from the raw data collected at observatories. In this paper I focus mainly on the problem of star-galaxy separation. For our purposes, the main difference between stars and galaxies is that stars look completely unresolved and so have sharp images, while galaxies look fuzzy. Even though stars are not fuzzy, images taken through telescopes have finite resolution and the stellar images do have non-zero sizes. All galaxies are centrally concentrated, and a significant fraction of galaxies have activity near their centers and so appear to have a stellar core surrounded by faint fuzz.
Pixels are one arcsecond.
The contrast has been enhanced to make visible both faint objects and the noise in the sky. Our goal is to develop a classification algorithm that can distinguish the stars from the galaxies. There are more than 3 million image patches this size across the entire sky. Two examples will suffice to demonstrate the scope of the problem. Accurate classification of this vast number of objects down to the detection limit of the survey is a daunting task that will require new classification methods.