19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
This addresses the problem of high-speed triggering in particle physics experiments, offering a domain-specific incremental improvement.
The paper tackled the need for lightweight neural networks for low-latency particle physics tasks by developing a symmetric architecture with only 19 parameters, which outperformed generic models with tens of thousands of parameters in top quark jet tagging.
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.