Abstract
Citizen scientists are becoming more and more important in helping professionals working through big data. An example in astronomy is crowd sourced galaxy classification. But how reliable are these classifications for studies of galaxy evolution? We have created a tool that allows us to use crowd sourced data from college students to investigate morphological classifications and compare the data against existing professional classifications. User generated data is prone to error and comparing said data against a professional classification allowed us to establish a margin of error, which we then corrected using machine learning. The program was coded using C++, the data was obtained and processed using Google’s Cloud Engine platform, and the charts allowing us to visualize the data were processed locally using excel.
Faculty Sponsors
Dr. Stefan Kautsch
Project Type
Event
Location
Alvin Sherman Library
Start Date
4-7-2017 12:00 AM
End Date
4-7-2017 12:00 AM
Galaxy Morphology Classification System
Alvin Sherman Library
Citizen scientists are becoming more and more important in helping professionals working through big data. An example in astronomy is crowd sourced galaxy classification. But how reliable are these classifications for studies of galaxy evolution? We have created a tool that allows us to use crowd sourced data from college students to investigate morphological classifications and compare the data against existing professional classifications. User generated data is prone to error and comparing said data against a professional classification allowed us to establish a margin of error, which we then corrected using machine learning. The program was coded using C++, the data was obtained and processed using Google’s Cloud Engine platform, and the charts allowing us to visualize the data were processed locally using excel.
