Graph neural networks for icecube signal classification

2018

Abstract

Tasks involving the analysis of geometric (graph-and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors spatial coordinates. As only a subset of IceCubes sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

Authors

Nicholas Choma
Federico Monti
Lisa Gerhardt
Tomasz Palczewski
Zahra Ronaghi
Prabhat Prabhat
Wahid Bhimji
Michael Bronstein
Spencer Klein
Joan Bruna