The large datasets and often low signal-To-noise inherent to the raw data of modern astroparticle experiments calls out for increasingly sophisticated event classification techniques. Machine learning algorithms, such as neural networks, have the potential to outperform traditional analysis methods, but come with the major challenge of identifying reliably classified training samples from real data. Citizen science represents an effective approach to sort through the large datasets efficiently and meet this challenge. Muon Hunter is a project hosted on the Zooniverse platform, wherein volunteers sort through pictures of data from the VERITAS cameras to identify muon ring images. Each image is classified multiple times to produce a clean dataset used to train and validate a convolutional neural network model both able to reject background events and identify suitable calibration data to monitor the telescope performance as a function of time.
|Original language||English (US)|
|Journal||Journal of Physics: Conference Series|
|State||Published - Jan 20 2020|
|Event||15th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2017 - Sudbury, Canada|
Duration: Jun 24 2017 → Jun 28 2017
Bibliographical notePublisher Copyright:
© Published under licence by IOP Publishing Ltd.