An End-to-end Architecture for Collider Physics and Beyond
🤖 AI Summary
This work addresses the fragmentation, reliance on specialized software packages, and challenges in automation and reproducibility that plague collider phenomenology in high-energy physics. We propose the first language-driven, end-to-end multi-agent system, built upon a hierarchical multi-agent reasoning architecture and a unified execution backend named Magnus. The system requires only natural language prompts and standard physics notation to execute the full workflow—from Lagrangian formulation to detector-level analysis—without any custom code. Its decoupled, domain-agnostic design enables cross-task generalization and reproducible research. We demonstrate its efficacy by successfully reproducing canonical scenarios involving leptoquarks, axion-like particles, and higher-dimensional effective operators, performing parton- and detector-level analyses, large-scale parameter scans, and exclusion limit generation, thereby validating the framework’s generality and robustness.
Application Category
📝 Abstract
We present, to our knowledge, the first language-driven agent system capable of executing end-to-end collider phenomenology tasks, instantiated within a decoupled, domain-agnostic architecture for autonomous High-Energy Physics phenomenology. Guided only by natural-language prompts supplemented with standard physics notation, ColliderAgent carries out workflows from a theoretical Lagrangian to final phenomenological outputs without relying on package-specific code. In this framework, a hierarchical multi-agent reasoning layer is coupled to Magnus, a unified execution backend for phenomenological calculations and simulation toolchains. We validate the system on representative literature reproductions spanning leptoquark and axion-like-particle scenarios, higher-dimensional effective operators, parton-level and detector-level analyses, and large-scale parameter scans leading to exclusion limits. These results point to a route toward more automated, scalable, and reproducible research in collider physics, cosmology, and physics more broadly.
Problem
Research questions and friction points this paper is trying to address.
collider physics
end-to-end automation
natural language prompting
phenomenology
reproducible research
Innovation
Methods, ideas, or system contributions that make the work stand out.
language-driven agent
end-to-end collider phenomenology
multi-agent reasoning
domain-agnostic architecture
automated physics simulation
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