mlsanityData to 7 Oct 2026

Graph Neural Networks for IceCube Signal Classification

arXiv 1809.06166 ↗First posted 17 Sep 2018cs.LG astro-ph.IM stat.MLcited by 24

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Abstract (arXiv)

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 IceCube's 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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2018.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 2; 2024: 3; 2025: 3; 2026: 1.

24 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  3. 2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
  4. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  5. 2022Astronomia ex machina: a history, primer, and outlook on neural networks in astronomycited by 17
  6. 2022Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Coloringscited by 15
  7. 2021A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatorycited by 13
  8. 2022Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCubecited by 8
  9. 2023Learning Adaptive Neighborhoods for Graph Neural Networkscited by 6
  10. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  11. 2023Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentationcited by 3
  12. 2021Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Arraycited by 2
12 more
  1. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  2. 2024Graph Neural Network for Neutrino Physics Event Reconstructioncited by 2
  3. 2021Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learningcited by 1
  4. 2025Deal: Distributed End-to-End GNN Inference for All Nodescited by 1
  5. 2021Data driven design of optical resonatorscited by 0
  6. 2022Towards Real-Time Temporal Graph Learningcited by 0
  7. 2022Rejecting noise in Baikal-GVD data with neural networkscited by 0
  8. 2024Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolutioncited by 0
  9. 2024Learning Efficient Representations of Neutrino Telescope Eventscited by 0
  10. 2025Tagging fully hadronic exotic decays of the vectorlike B quark using a graph neural networkcited by 0
  11. 2025NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopescited by 0
  12. 2026Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstructioncited by 0