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The increasing importance of digital sky surveys collecting many millions ofgalaxy images has reinforced the need for robust methods that can performmorphological analysis of large galaxy image databases. Citizen scienceinitiatives such as Galaxy Zoo showed that large datasets of galaxy images canbe analyzed effectively by non-scientist volunteers, but since databasesgenerated by robotic telescopes grow much faster than the processing power ofany group of citizen scientists, it is clear that computer analysis isrequired. Here we propose to use citizen science data for training machinelearning systems, and show experimental results demonstrating that machinelearning systems can be trained with citizen science data. Our findings showthat 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 agreementbetween the citizen scientists. The source code of the method is publiclyavailable.

Combining human and machine learning for morphological analysis of galaxy images