Speaker
Description
State-of-the-art machine learning models in particle physics, such as PELICAN [1] and Particle Transformer [2], exhibit disadvantageous $\mathcal{O}(N^2)$ scaling with the multiplicity $N$ of the jets. Fundamentally, this has to do with the tendency for highly expressive models to involve complicated permutation-equivariant layers to model pairwise interactions. Similar limitations arise in other fields that use graph neural networks, such as chemistry. I will present a radically simpler architecture, CARDINAL, which balances universal expressivity and ultra-low computational cost by employing a permutation-invariant (and also Lorentz-invariant) embedding which allows for $\mathcal{O}(N)$ scaling. In common benchmark tests, it is tens or hundreds of times faster and less memory-demanding than comparable popular architectures.
[1] Alexander Bogatskiy, Timothy Hoffman, David W. Miller, Jan T. Offermann & Xiaoyang Liu, Explainable equivariant neural networks for particle physics: PELICAN, 2024.
[2] Huilin Qu, Congqiao Li, Sitian Qian, Particle Transformer for Jet Tagging, 2022.