DATA MINING THE GALAXY ZOO MERGERS

The goal is to determine indicators of merger status based solely on discovering those automated pipeline-generated attributes in the astronomical database that correlate most strongly with the patterns identified through visual inspection by the Galaxy Zoo volunteers. In the end, we aim to provide a new and improved automated procedure for classification of collisions and mergers in future petascale astronomical sky surveys.

Both information gain analysis (via the C4.5 decision tree algorithm) and cluster analysis (via the Davies-Bouldin Index) are explored as techniques for finding the strongest correlations between human-identified patterns and existing database attributes. Galaxy attributes measured in the SDSS green waveband images are found to represent the most influential of the attributes for correct classification of collisions and mergers.

Only a nominal information gain is noted in this research, however, there is a clear indication of which attributes contribute so that a direction for further study is apparent.