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

Author:

Alexander Bogatskiy, Timothy Hoffman, Jan T. Offermann

Keyword:

High Energy Physics - Phenomenology, High Energy Physics - Phenomenology (hep-ph), Machine Learning (cs.LG), High Energy Physics - Experiment (hep-ex)

journal:

date:

2023-10-23 16:00:00

Abstract

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.

PDF: 19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics.pdf