The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Jet tagging via particle clouds
Open accessAbstract
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a ``particle cloud.'' Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Aggregated Residual Transformations for Deep Neural Networks
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Cited by
Graph convolutional networks: a comprehensive review
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Metadata
- Authors
- Qu, H., & Gouskos, L.
- Venue
- Physical Review D
- Publisher
- American Physical Society (APS)
- Published
- 2020-03-26
- Volume
- 101
- Issue
- 5
- ISSN
- 2470-0010 (ISSN-L)
- 2470-0029
- OpenAlex
- W2915621743
- Cited by
- 425
- References
- 60
- FWCI
- 21.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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