Independent component analysis for artefact separation in astrophysical images.

In this paper, we demonstrate that independent component analysis, a novel signal processing technique, is a powerful method for separating artefacts from astrophysical image data. When studying far-out galaxies from a series of consequent telescope images, there are several sources for artefacts that influence all the images, such as camera noise, atmospheric fluctuations and disturbances, cosmic rays, and stars in our own galaxy. In the analysis of astrophysical image data it is very important to implement techniques which are able to detect them with great accuracy, to avoid the possible physical events from being eliminated from the data along with the artefacts. For this problem, the linear ICA model holds very accurately because such artefacts are all theoretically independent of each other and of the physical events. Using image data on the M31 Galaxy, it is shown that several artefacts can be detected and recognized based on their temporal pixel luminosity profiles and independent component images. The obtained separation is good and the method is very fast. It is also shown that ICA outperforms principal component analysis in this task. For these reasons, ICA might provide a very useful pre-processing technique for the large amounts of available telescope image data.