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Volume 358 - 36th International Cosmic Ray Conference (ICRC2019) - NU - Neutrino
Application of Deep Neural Networks to Event Type Classification in IceCube
M. Kronmueller,* G. Theo
*corresponding author
Full text: pdf
Pre-published on: 2019 July 22
Published on:
Abstract
The IceCube Neutrino Observatory is able to measure the all-flavor neutrino flux in the energy range between 100 GeV and several PeV. Due to the different features of the neutrino interactions and the geometry of the detector, all high-level analyses require a selection of suitable events as a first step. However, presently, no algorithm exists that gives a generic prediction of an event’s underlying interaction type. One possible solution to this is the use of deep neural networks similar to the ones commonly used for 2D image recognition. The classifier that we present here is based on the modern InceptionResNet architecture and includes multi-task learning in order to broaden the field of application and increase the overall accuracy of the result. We provide a detailed discussion of the network’s architecture, examine the performance of the classifier for event type classification and explain possible applications in IceCube.
Open Access
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