# NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes

arXiv: https://arxiv.org/abs/2511.13111 · HTML: https://mlsanity.com/paper/2511.13111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 114 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Graph Inductive Biases in Transformers without Message Passing](https://mlsanity.com/paper/2305.17589/) (2023)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [ParticleNet: Jet Tagging via Particle Clouds](https://mlsanity.com/paper/1902.08570/) (2019)
- [A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking](https://mlsanity.com/paper/2210.07494/) (2022)
- [OmniJet-α: The first cross-task foundation model for particle physics](https://mlsanity.com/paper/2403.05618/) (2024)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs](https://mlsanity.com/paper/1812.04616/) (2018)
- [Graph Neural Networks for IceCube Signal Classification](https://mlsanity.com/paper/1809.06166/) (2018)
- [A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory](https://mlsanity.com/paper/2101.11589/) (2021)
- [Benchmarking Positional Encodings for GNNs and Graph Transformers](https://mlsanity.com/paper/2411.12732/) (2024)
- [Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube](https://mlsanity.com/paper/2209.03042/) (2022)
- [Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube](https://mlsanity.com/paper/2107.12110/) (2021)
- [Observation of high-energy neutrinos from the Galactic plane](https://mlsanity.com/paper/2307.04427/) (2023)
- [GraphNeT: Graph neural networks for neutrino telescope event reconstruction](https://mlsanity.com/paper/2210.12194/) (2022)
- [Conditional normalizing flows for IceCube event reconstruction](https://mlsanity.com/paper/2309.16380/) (2023)

## Abstract (arXiv)

Neutrino telescopes are large-scale detectors designed to observe Cherenkov radiation produced from neutrino interactions in water or ice. They exist to identify extraterrestrial neutrino sources and to probe fundamental questions pertaining to the elusive neutrino itself. A central challenge common across neutrino telescopes is to solve a series of inverse problems known as event reconstruction, which seeks to resolve properties of the incident neutrino, based on the detected Cherenkov light. In recent times, significant efforts have been made in adapting advances from deep learning research to event reconstruction, as such techniques provide several benefits over traditional methods. While a large degree of similarity in reconstruction needs and low-level data exists, cross-experimental collaboration has been hindered by a lack of diverse open-source datasets for comparing methods. We present NuBench, an open benchmark for deep learning-based event reconstruction in neutrino telescopes. NuBench comprises seven large-scale simulated datasets containing nearly 130 million charged- and neutral-current muon-neutrino interactions spanning 10 GeV to 100 TeV, generated across six detector geometries inspired by existing and proposed experiments. These datasets provide pulse- and event-level information suitable for developing and comparing machine-learning reconstruction methods in both water and ice environments. Using NuBench, we evaluate four reconstruction algorithms - ParticleNeT and DynEdge, both actively used within the KM3NeT and IceCube collaborations, respectively, along with GRIT and DeepIce - on up to five core tasks: energy and direction reconstruction, topology classification, interaction vertex prediction, and inelasticity estimation.