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

arXiv: https://arxiv.org/abs/2101.11589 · HTML: https://mlsanity.com/paper/2101.11589/

## 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 (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [HexaConv](https://mlsanity.com/paper/1803.02108/) (2018)
- [A Convolutional Neural Network Neutrino Event Classifier](https://mlsanity.com/paper/1604.01444/) (2016)
- [Graph Neural Networks for IceCube Signal Classification](https://mlsanity.com/paper/1809.06166/) (2018)

## Built on by (most cited first, 13 of 13)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube](https://mlsanity.com/paper/2209.03042/) (2022)
- [Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks](https://mlsanity.com/paper/2303.08812/) (2023)
- [Bias and Priors in Machine Learning Calibrations for High Energy Physics](https://mlsanity.com/paper/2205.05084/) (2022)
- [GraphNeT: Graph neural networks for neutrino telescope event reconstruction](https://mlsanity.com/paper/2210.12194/) (2022)
- [Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere](https://mlsanity.com/paper/2604.19846/) (2026)
- [Domain-informed neural networks for interaction localization within astroparticle experiments](https://mlsanity.com/paper/2112.07995/) (2021)
- [Neutrino Reconstruction in TRIDENT Based on Graph Neural Network](https://mlsanity.com/paper/2401.15324/) (2024)
- [Learning Efficient Representations of Neutrino Telescope Events](https://mlsanity.com/paper/2410.13148/) (2024)
- [NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes](https://mlsanity.com/paper/2511.13111/) (2025)
- [Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks](https://mlsanity.com/paper/2602.23321/) (2026)
- [Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction](https://mlsanity.com/paper/2606.02788/) (2026)

## 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.