Mining the Galaxy Zoo Database: Machine Learning Applications

Details Mining the Galaxy Zoo Database: Machine Learning Applications Mining the Galaxy Zoo Database: Machine Learning Applications

Jan 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21543528b&link_type=abstract

American Astronomical Society, AAS Meeting #215, #435.28; Bulletin of the American Astronomical Society, Vol. 42, p.383

Mathematics

Logic

Scientific paper

The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

Affiliated with

Also associated with

No associations

Rating

Mining the Galaxy Zoo Database: Machine Learning Applications does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Mining the Galaxy Zoo Database: Machine Learning Applications, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Mining the Galaxy Zoo Database: Machine Learning Applications will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-968577