Citizen Scientists Probe Early Galaxies, Sky and Telescope
"Now, astronomers using data collected by Galaxy Zoo -- a crowd-sourced astronomy project that invites the public to analyze fuzzy images of distant galaxies -- are peering deeper into the universe in search of these barred galaxies. "Galaxy Zoo works because spotting features in galaxies is a task well suited to humans. We as a species are great at pattern recognition," says project astronomer Brooke Simmons (Oxford, U.K.). "And you don't need to be an astrophysicist to recognize a boxy shape inside a rounded disk."
"The increasing importance of digital sky surveys collecting many millions of galaxy images has reinforced the need for robust methods that can perform morphological analysis of large galaxy image databases. Citizen science initiatives such as Galaxy Zoo showed that large datasets of galaxy images can be analyzed effectively by non-scientist volunteers, but since databases generated by robotic telescopes grow much faster than the processing power of any group of citizen scientists, it is clear that computer analysis is required. Here we propose to use citizen science data for training machine learning systems, and show experimental results demonstrating that machine learning systems can be trained with citizen science data. Our findings show that the performance of machine learning depends on the quality of the data, which can be improved by using samples that have a high degree of agreement between the citizen scientists. The source code of the method is publicly available."