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Event classification is a common task in gamma-ray astrophysics. It can betreated with rapidly-advancing machine learning algorithms, which have thepotential to outperform traditional analysis methods. However, a majorchallenge for machine learning models is extracting reliably labelled trainingexamples from real data. Citizen science offers a promising approach to tacklethis challenge.  We present "Muon Hunter", a citizen science project hosted on the Zooniverseplatform, where VERITAS data are classified multiple times by individual usersin order to select and parameterize muon events, a product from cosmic rayinduced showers. We use this dataset to train and validate a convolutionalneural-network model to identify muon events for use in monitoring andcalibration. The results of this work and our experience of using theZooniverse are presented.

A citizen-science approach to muon events in imaging atmospheric Cherenkov telescope data: the Muon Hunter