  Muons from extensive air showers appear as rings in images taken with imaging
atmospheric Cherenkov telescopes, such as VERITAS. These muon-ring images are
used for the calibration of the VERITAS telescopes, however the calibration
accuracy can be improved with a more efficient muon-identification algorithm.
Convolutional neural networks (CNNs) are used in many state-of-the-art
image-recognition systems and are ideal for muon image identification, once
trained on a suitable dataset with labels for muon images. However, by training
a CNN on a dataset labelled by existing algorithms, the performance of the CNN
would be limited by the suboptimal muon-identification efficiency of the
original algorithms. Muon Hunters 2 is a citizen science project that asks
users to label grids of VERITAS telescope images, stating which images contain
muon rings. Each image is labelled 10 times by independent volunteers, and the
votes are aggregated and used to assign a `muon' or `non-muon' label to the
corresponding image. An analysis was performed using an expert-labelled dataset
in order to determine the optimal vote percentage cut-offs for assigning labels
to each image for CNN training. This was optimised so as to identify as many
muon images as possible while avoiding false positives. The performance of this
model greatly improves on existing muon identification algorithms, identifying
approximately 30 times the number of muon images identified by the current
algorithm implemented in VEGAS (VERITAS Gamma-ray Analysis Suite), and roughly
2.5 times the number identified by the Hough transform method, along with
significantly outperforming a CNN trained on VEGAS-labelled data.
