Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC

By Savannah Thais, Gage DeZoort. 2021

7 citations recorded by the source corpus.

3D instance segmentation remains a challenging problem in computer vision. Particle tracking at colliders like the LHC can be conceptualized as an instance segmentation task: beginning from a point cloud of hits in a particle detector, an algorithm must identify which hits belong to individual particle trajectories and extract track properties. Graph Neural Networks (GNNs) have shown promising performance on standard instance segmentation tasks. In this work we demonstrate the applicability of instance segmentation GNN architectures to particle tracking; moreover, we re-imagine the traditional Cartesian space approach to track-finding and instead work in a conformal geometry that allows the GNN to identify tracks and extract parameters in a single shot.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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In Computer Vision and Pattern Recognition

1. Tong Lu
2. Wenhai Wang
3. Yirui Wu
4. Guangchen Shi
5. J. Liu
6. Shaohua Wan
7. Luc Van Gool
8. Yu Qiao

Citations in the corpus

• Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation (2022, methodology): “Using topological structure of graphs, GNN-based methods [30, 32, 42] transform task of image segmentation into the classification task of graph nodes.”