Rediscovering the Standard Model with AI

TL;DR

This paper demonstrates that AI techniques can autonomously rediscover fundamental structures and symmetries of the Standard Model of particle physics solely from experimental data, without prior theoretical input.

Contribution

The study shows that unsupervised machine learning can uncover key features, symmetries, and organizational principles of the Standard Model directly from particle data.

Findings

Uncovered interaction strengths and particle classifications

Identified conserved quantities like baryon number and strangeness

Detected patterns consistent with Regge trajectories

Abstract

We investigate whether artificial intelligence can autonomously recover known structures of the Standard Model of particle physics using only experimental data and without theoretical inputs. By applying unsupervised machine learning techniques -- including data dimensionality reduction and clustering algorithms -- to intrinsic particle properties and decay modes, we uncover key organizational features of particle physics, such as the relative strength of different interactions and the difference between baryons and mesons. We also identify conserved quantities such as baryon number, strangeness and charm as well as the structure of isospin and the Eightfold Way multiplets. Our analysis then reveals that clustering can separate particles by interaction, flavor symmetries as well as quantum numbers. Additionally, we observe patterns consistent with Regge trajectories in baryon excitations. Our results demonstrate that machine learning can reproduce key aspects of the Standard Model directly from data, suggesting a promising path toward data-driven discovery in fundamental physics.