BEXCO - Room F(201/202/203/204)
[GA105] A citizen-science approach to muon events in VHE data: the Muon Hunter
The ability to separate cosmic-ray (CR) particles from gamma rays is important for imaging atmospheric Cherenkov telescopes (IACTs), as it is directly related to the sensitivity of the instrument. The event classification problems in IACT data analysis can be treated with rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional analysis methods using image parameters. However, a major challenge for machine learning models is to extract 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 CR induced showers. We use this dataset to train and validate a convolutional neural networks model to identify muon events for use in monitoring and callibration. The results of this work and our experience of using the Zooniverse are presented.