# 19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics

arXiv: https://arxiv.org/abs/2310.16121 · HTML: https://mlsanity.com/paper/2310.16121/

## 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.

## Builds on (most cited first, 10 of 10)

- [Invariant and Equivariant Graph Networks](https://mlsanity.com/paper/1812.09902/) (2018)
- [Lorentz Group Equivariant Neural Network for Particle Physics](https://mlsanity.com/paper/2006.04780/) (2020)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Energy Flow Networks: Deep Sets for Particle Jets](https://mlsanity.com/paper/1810.05165/) (2018)
- [PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics](https://mlsanity.com/paper/2211.00454/) (2022)
- [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://mlsanity.com/paper/2307.16506/) (2023)
- [Jet Constituents for Deep Neural Network Based Top Quark Tagging](https://mlsanity.com/paper/1704.02124/) (2017)
- [LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics](https://mlsanity.com/paper/2209.14065/) (2022)
- [BIP: Boost Invariant Polynomials for Efficient Jet Tagging](https://mlsanity.com/paper/2207.08272/) (2022)
- [Feature Selection with Distance Correlation](https://mlsanity.com/paper/2212.00046/) (2022)

## 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.