19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
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Abstract (arXiv)
As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architectures for low-latency tasks such as triggering. We examine the potential of one recent Lorentz- and permutation-symmetric architecture, PELICAN, and present its instances with as few as 19 trainable parameters that outperform generic architectures with tens of thousands of parameters when compared on the binary classification task of top quark jet tagging.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 24 Oct 2023.
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References · 10
- 2018Invariant and Equivariant Graph Networkscited by 346
- 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 121
- 2022Particle Transformer for Jet Taggingcited by 98
- 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 84
- 2022PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physicscited by 26
- 2023Explainable Equivariant Neural Networks for Particle Physics: PELICANcited by 24
- 2017Jet Constituents for Deep Neural Network Based Top Quark Taggingcited by 15
- 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
- 2022BIP: Boost Invariant Polynomials for Efficient Jet Taggingcited by 7
- 2022Feature Selection with Distance Correlationcited by 3
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