# Topological Reconstruction of Particle Physics Processes using Graph Neural Networks

arXiv: https://arxiv.org/abs/2303.13937 · HTML: https://mlsanity.com/paper/2303.13937/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [ParticleNet: Jet Tagging via Particle Clouds](https://mlsanity.com/paper/1902.08570/) (2019)
- [Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks](https://mlsanity.com/paper/2010.09206/) (2020)
- [Jet tagging in the Lund plane with graph networks](https://mlsanity.com/paper/2012.08526/) (2020)
- [Charged particle tracking via edge-classifying interaction networks](https://mlsanity.com/paper/2103.16701/) (2021)
- [SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention](https://mlsanity.com/paper/2106.03898/) (2021)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)

## Built on by (most cited first, 8 of 8)

- [nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://mlsanity.com/paper/2307.02405/) (2023)
- [Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks](https://mlsanity.com/paper/2309.01886/) (2023)
- [Reconstructing short-lived particles using hypergraph representation learning](https://mlsanity.com/paper/2402.10149/) (2024)
- [PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction](https://mlsanity.com/paper/2402.11538/) (2024)
- [Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems](https://mlsanity.com/paper/2605.22891/) (2026)
- [Pairton: Iterative Reconstruction of Short-Lived Particles](https://mlsanity.com/paper/2608.14278/) (2026)
- [Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors](https://mlsanity.com/paper/2603.23356/) (2026)
- [Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion](https://mlsanity.com/paper/2609.18928/) (2026)

## Abstract (arXiv)

We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks. The Topograph not only solves the combinatoric assignment of observed final state objects, associating them to their original mother particles, but directly predicts the properties of intermediate particles in hard scatter processes and their subsequent decays. In comparison to standard combinatoric approaches or modern approaches using graph neural networks, which scale exponentially or quadratically, the complexity of Topographs scales linearly with the number of reconstructed objects. We apply Topographs to top quark pair production in the all hadronic decay channel, where we outperform the standard approach and match the performance of the state-of-the-art machine learning technique.