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30 May 2022 to 4 June 2022
Virtual Seoul
Asia/Seoul timezone

Fast Reconstruction Using Convolutional Neural Networks for Neutrino Oscillation on IceCube

Not scheduled
5m
Virtual Seoul

Virtual Seoul

Poster Neutrino oscillation Poster

Speaker

Jessie Micallef (Michigan State University)

Description

The IceCube Neutrino Observatory, a gigaton ice Cherenkov detector located at the South Pole, detects a high rate of atmospheric neutrinos. The DeepCore array extends IceCube’s detection of atmospheric neutrinos down to GeV-scales, which is the range necessary to measure neutrino oscillations. With the high statistics atmospheric neutrino sample, the reconstruction of GeV-scale IceCube neutrinos must be both accurate and computationally fast. Thus, Convolution Neural Networks (CNNs) were developed and optimized to reconstruct GeV-scale neutrino properties in the IceCube DeepCore detector such as energy, direction of travel, and interaction vertex. Additionally, CNNs were trained to distinguish particle topologies in the detector, to create a full suite of reconstruction parameters needed for a neutrino oscillation analysis on IceCube. Their performance has similar accuracy to leading likelihood-based reconstruction methods with the CNNs providing $10^4$x speed improvement.

Collaboration IceCube

Primary authors

Jessie Micallef (Michigan State University) Shiqi Yu (Michigan State University)

Presentation Materials