# Learning representations of irregular particle-detector geometry with distance-weighted graph networks

arXiv: https://arxiv.org/abs/1902.07987 · HTML: https://mlsanity.com/paper/1902.07987/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 30 of 43)

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- [FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics](https://mlsanity.com/paper/2508.14087/) (2025)
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- [TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography](https://mlsanity.com/paper/2309.14027/) (2023)
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## Abstract (arXiv)

We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.