Speaker
Description
Measurements of neutrinos at and below 10 GeV provides unique constraints of neutrino oscillation parameters as well as probes of potential Non-Standard Interactions (NSI). The IceCube Neutrino Observatory’s DeepCore array is designed to detect neutrinos down to GeV scales. This work uses convolutional neural networks (CNNs) to improve the energy reconstruction resolution and speed of reconstructing 10-GeV scale neutrino events in IceCube. Compared to past likelihood-based methods which take seconds to minutes, the CNN is expected to provide approximately a factor of 2 improvement in energy resolution and reconstructs in milliseconds which is a great advantage for large datasets.
Keywords
neural network, convolutional neutral network, CNN, machine learning, reconstruction
| Subcategory | Experimental Methods & Instrumentation |
|---|---|
| Collaboration | IceCube |
