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A citizen-science approach to muon events in imaging atmospheric Cherenkov telescope data: the Muon Hunter
Event classification is a common task in gamma-ray astrophysics. It can be
treated with rapidly-advancing machine learning algorithms, which have the
potential to outperform traditional analysis methods. However, a major
challenge for machine learning models is extracting reliably labelled training
examples from real data. Citizen science offers a promising approach to tackle
this challenge.
We present "Muon Hunter", a citizen science project hosted on the Zooniverse
platform, where VERITAS data are classified multiple times by individual users
in order to select and parameterize muon events, a product from cosmic ray
induced showers. We use this dataset to train and validate a convolutional
neural-network model to identify muon events for use in monitoring and
calibration. The results of this work and our experience of using the
Zooniverse are presented.Comment: 8 pages, 3 figures, in Proceedings of the 35th International Cosmic
Ray Conference (ICRC 2017), Busan, South Kore
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