Abstract
During five years of observations, the Hobby-Eberly Dark Energy Experiment (HETDEX) will find over one million galaxies that are 9 billion to 11 billion light-years away, and will yield the largest map of the universe ever produced. The map will allow us to measure how fast the universe was expanding at different times in its history. Changes in the expansion rate will reveal the role of dark energy at different epochs, but the most difficult task is determining which galaxies come from our epoch of interest (at z~2-3) and which ones are nearby. These one million sources require visual classification for the most accurate results, yet this requires many working hours. Over the past year, I have used Zooniverse, the worlds largest citizen science platform, to train participants all over the world to efficiently classify these objects. The project has been live for less than 2 months and we have already had 750,000 classificiations. I will discuss the methods we have used and how citizen science can be applied to big data astronomy and preliminary applications to machine learning.
