Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.28935 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914432987168768 |
|---|---|
| author | Alexander, Stephon Bradley, Benjamin Gouskos, Loukas Niu, Cooper |
| author_facet | Alexander, Stephon Bradley, Benjamin Gouskos, Loukas Niu, Cooper |
| contents | The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce \textsc{Albert}, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory space. By encoding particle physics as a formal language, \textsc{Albert} generates tokenized sequences representing symmetries, particles, and interactions under a rule-based grammar, eliminating the hallucinations common in large language models. The reinforcement learning environment enforces first-principle theoretical constraints, computes observables with radiative corrections, and evaluates statistical likelihood via $χ^2$ analysis against experimental data. As a proof of concept, we train a 25-million-parameter transformer model using only legacy data from the Large Electron-Positron Collider, which contains no direct evidence of the top quark. Remarkably, \textsc{Albert} successfully rediscovered the Standard Model and autonomously inferred necessity and properties of the top quark, predicting its mass at $178.9\pm 5.0~\text{GeV}$, consistent with its modern measurement at the Large Hadron Collider. These results demonstrate the potential of AI-driven theory exploration as a rigorous, hallucination-free, and scalable paradigm for autonomous discovery of new physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28935 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Autonomous Discovery of Particle Physics Theories from Experimental Data Alexander, Stephon Bradley, Benjamin Gouskos, Loukas Niu, Cooper High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce \textsc{Albert}, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory space. By encoding particle physics as a formal language, \textsc{Albert} generates tokenized sequences representing symmetries, particles, and interactions under a rule-based grammar, eliminating the hallucinations common in large language models. The reinforcement learning environment enforces first-principle theoretical constraints, computes observables with radiative corrections, and evaluates statistical likelihood via $χ^2$ analysis against experimental data. As a proof of concept, we train a 25-million-parameter transformer model using only legacy data from the Large Electron-Positron Collider, which contains no direct evidence of the top quark. Remarkably, \textsc{Albert} successfully rediscovered the Standard Model and autonomously inferred necessity and properties of the top quark, predicting its mass at $178.9\pm 5.0~\text{GeV}$, consistent with its modern measurement at the Large Hadron Collider. These results demonstrate the potential of AI-driven theory exploration as a rigorous, hallucination-free, and scalable paradigm for autonomous discovery of new physics. |
| title | Autonomous Discovery of Particle Physics Theories from Experimental Data |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory |
| url | https://arxiv.org/abs/2603.28935 |