# Graph Neural Networks for Particle Tracking and Reconstruction

@article{Duarte2020GraphNN, title={Graph Neural Networks for Particle Tracking and Reconstruction}, author={J. Duarte and J. Vlimant}, journal={arXiv: High Energy Physics - Phenomenology}, year={2020} }

Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle signatures from raw detector data. In order to benefit from modern deep learning algorithms that were initially designed for computer vision or natural language processing tasks, it is common practice to transform HEP data into images or sequences. Conversely, graph neural networks (GNNs), which operate on graph data… Expand

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