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Modern astronomy relies on massive databases collected by robotic telescopesand digital sky surveys, acquiring data in a much faster pace than what manualanalysis can support. Among other data, these sky surveys collect informationabout millions and sometimes billions of extra-galactic objects. Since the verylarge number of objects makes manual observation impractical, automatic methodsthat can analyze and annotate extra-galactic objects are required to fullyutilize the discovery power of these databases. Machine learning methods forannotation of celestial objects can be separated broadly into methods that usethe photometric information collected by digital sky surveys, and methods thatanalyze the image of the object. Here we describe a hybrid method that combinesphotometry and image data to annotate galaxies by their morphology, and amethod that uses that information to identify objects that are visually similarto a query object (query-by-example). The results are compared to using justphotometric information from SDSS, and to using just the morphologicaldescriptors extracted directly from the images. The comparison shows that forautomatic classification the image data provide marginal addition to theinformation provided by the photometry data. For query-by-example, however, theanalysis of the image data provides more information that improves theautomatic detection substantially. The source code and binaries of the methodcan be downloaded through the Astrophysics Source Code Library.

A hybrid approach to machine learning annotation of large galaxy image databases