Characterizing the Effects of Model Parameters on Performance of Convolutional Neural Networks for Cosmic Ray Shower Reconstruction
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Author
Roy WoodDate
2020
Metadata
Show full item recordTitle
Characterizing the Effects of Model Parameters on Performance of Convolutional Neural Networks for Cosmic Ray Shower ReconstructionAbstract
In collaboration with IceCube South Pole Neutrino Observatory, data from over 400,000 cosmic ray energy shower events recorded by IceTop, the surface component of IceCube, were used to train an array of convolutional neural network (CNN) models that reconstruct the initial energy of the cosmic ray primary.Collections