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# The XXX International Conference on Neutrino Physics and Astrophysics (Neutrino 2022)

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

### 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)