http://arxiv.org/abs/1708.06393
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.
Q. Feng, VERITAS. Collaboration and J. Jarvis
Wed, 23 Aug 17
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Comments: 8 pages, 3 figures, in Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, South Korea