mlsanityData to 14 Oct 2026

A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

arXiv 2101.11589 ↗First posted 27 Jan 2021hep-ex cs.LGcited by 13

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Abstract (arXiv)

Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 15 of its 57 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

15 of 57 references.

References · 15

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3 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 2; 2025: 1; 2026: 3.

13 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  2. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  3. 2022Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCubecited by 8
  4. 2023Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networkscited by 4
  5. 2022Bias and Priors in Machine Learning Calibrations for High Energy Physicscited by 3
  6. 2022GraphNeT: Graph neural networks for neutrino telescope event reconstructioncited by 3
  7. 2026Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the spherecited by 1
  8. 2021Domain-informed neural networks for interaction localization within astroparticle experimentscited by 0
  9. 2024Neutrino Reconstruction in TRIDENT Based on Graph Neural Networkcited by 0
  10. 2024Learning Efficient Representations of Neutrino Telescope Eventscited by 0
  11. 2025NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopescited by 0
  12. 2026Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networkscited by 0
1 more
  1. 2026Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstructioncited by 0