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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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Bird, R. ; Daniel, M. K. ; Dickinson, H. ; Feng, Q. ; Fortson, L. ; Furniss, A. ; Jarvis, J. ; Mukherjee, R. ; Ong, R. ; Sadeh, I. ; et al ( , Journal of Physics: Conference Series)
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Ahnen, M. L. ; Ansoldi, S. ; Antonelli, L. A. ; Antoranz, P. ; Babic, A. ; Banerjee, B. ; Bangale, P. ; Barres de Almeida, U. ; Barrio, J. A. ; Becerra González, J. ; et al ( , Astronomy & Astrophysics)