# Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

arXiv: https://arxiv.org/abs/2107.12110 · HTML: https://mlsanity.com/paper/2107.12110/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [Observation of high-energy neutrinos from the Galactic plane](https://mlsanity.com/paper/2307.04427/) (2023)
- [Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere](https://mlsanity.com/paper/2604.19846/) (2026)
- [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://mlsanity.com/paper/2408.08474/) (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)

## Abstract (arXiv)

The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube.