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null (Ed.)In 2017, the Muon Hunter project on the Zooniverse.org citizen science platform successfully gathered more than two million classification labels for nearly 140,000 camera images from VER- ITAS. The aim was to select and parameterize muon events for use in training convolutional neural networks. The success of this project proved that crowdsourcing labels for IACT image analy- sis is a viable avenue for further development of advanced machine-learning algorithms. These algorithms could potentially lend themselves to improving class separation between gamma-ray and hadronic event types. Nonetheless, it took two months to gather these labels from volun- teers, which could be a bottleneck for future applications of this method. Here we present Muon Hunters 2.0: the follow-on project that demonstrates the development of unsupervised clustering techniques to gather muon labels more efficiently from volunteer classifiers.more » « less
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Milhone, J. ; Flanagan, K. ; Nornberg, M. D. ; Tabbutt, M. ; Forest, C. B. ( , Review of Scientific Instruments)
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Olson, J. ; Egedal, J. ; Greess, S. ; Myers, R. ; Clark, M. ; Endrizzi, D. ; Flanagan, K. ; Milhone, J. ; Peterson, E. ; Wallace, J. ; et al ( , Physical Review Letters)
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Cooper, C. M. ; Weisberg, D. B. ; Khalzov, I. ; Milhone, J. ; Flanagan, K. ; Peterson, E. ; Wahl, C. ; Forest, C. B. ( , Physics of Plasmas)