# 2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore

arXiv: https://arxiv.org/abs/2307.16373 · HTML: https://mlsanity.com/paper/2307.16373/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers](https://mlsanity.com/paper/2603.22342/) (2026)

## Abstract (arXiv)

IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction.