Computer Science – Learning
Scientific paper
Jun 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aspc..423..217w&link_type=abstract
Galaxy Wars: Stellar Populations and Star Formation in Interacting Galaxies ASP Conference Series Vol. 423, proceedings of a con
Computer Science
Learning
1
Scientific paper
The huge data volumes across the sciences require us to consider new approaches to data analysis and simulations. In this project, we investigate how citizen scientists from GalaxyZoo.org can be tasked to address this data flood by providing the human input to classify simulations of galaxy interactions. The primary science goal in the galaxy collision project is developing numerical models for hundreds of interacting systems. With these simulations, we will determine if these collisions are unique locations in the multi-parameter phase space that defines the input parameters to our simulations. We will also correlate these dynamical parameters with the star formation and nuclear activity in these systems. Further self-consistent, high resolution models will follow from these simple matches to compare simulations directly with the observations. A secondary goal of the galaxy collision project is to create a set of human-selected matches between simulations and models which can be used as a training set for machine learning algorithms. Analysis of this data set will allow the creation of a robust fitness function for evolutionary computing algorithms that can be used to model other interacting systems.
Bamford Steven
Borne Kirk
Fortson Lucy
Holincheck Anthony
Lintott Chris
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