A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2
2021
Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)
unpublished
10 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-ofthe-art image-recognition systems and are ideal for muon image identification, once trained on a suitable dataset with
doi:10.22323/1.395.0766
fatcat:wqwhumqsjbgvpfnoqmv7tiiwp4