Muon Hunting with the Crowd: Combining Humans and Machines to Solve Big Data Problems

In this presentation, I will describe the Zooniverse.org citizen science platform as a tool to gather labels from over 2.7 million dedicated volunteers worldwide who are motivated to participate in scientific research. Hundreds of research teams now turn to Zooniverse for crowdsourcing tasks such as image classification and annotation, which provide the large labeled data sets needed for optimal training of machine algorithms. I will demonstrate the ease with which a project can be developed with the Zooniverse Project Builder tools and describe the infrastructure available for integrating machine learning with Zooniverse including sophisticated active learning techniques. I will provide examples from across several projects in astronomy and astroparticle physics, with a focus on the Muon Hunter project used to gather millions of labels to train a machine algorithm for an imaging air Cherenkov telescope calibration pipeline. I will finish with a look at how we are working to combine human and machine intelligence to probe large data sets, including those from Cherenkov telescopes, for scientifically interesting anomalous events.