Conference on Intelligent Data Understanding 2010 (CIDU)

DATA MINING THE GALAXY ZOO MERGERS

STEVEN BAEHR, ARUN VEDACHALAM, KIRK BORNE, AND DANIEL SPONSELLER

Abstract. Collisions between pairs of galaxies usually end in the coalescence (merger) of the two
galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of
most galaxies, including our own Milky Way. With the onset of massive collection of astronomical
data, a computerized and automated method will be necessary for identifying those colliding
galaxies worthy of more detailed study. This project researches methods to accomplish that goal.
Astronomical data from the Sloan Digital Sky Survey (SDSS) and human-provided classifications
on merger status from the Galaxy Zoo project are combined and processed with machine learning
algorithms. 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.