SMEFT-现象学智能体：一种由自然语言驱动的人工智能智能体，用于机器学习辅助的标准模型有效场论现象学
SMEFT-Pheno-Agent: a natural-language-driven AI agent for machine-learning-assisted Standard Model Effective Field Theory phenomenology
浏览论文内容
中文总结 AI 辅助
介绍SMEFT-现象学智能体这一Python工作流程，由自然语言AI引导，用于高能对撞机的SMEFT现象学研究。它协调多阶段，自动生成参数文件等，委托工具计算，记录相关工件，建立了可重复性和审核可追溯性。
中文摘要 AI 辅助
我们展示了SMEFT-现象学智能体，这是一个由自然语言人工智能智能体引导的Python工作流程，用于在高能对撞机上进行机器学习辅助的标准模型有效场论（SMEFT）现象学研究。该软件协调十二个自动执行阶段，涵盖配置输入、环境验证、事件生成、机器学习选择、统计推断和最终审核。在每个阶段边界，智能体解释自然语言意图以生成后续执行所需的可运行参数文件和适配器调用。一旦写入探测器级事件，智能体就会自动提出关键运动学可观测量以及适合特定数据结构和分析目标的候选机器学习算法。所有数值计算都严格委托给经过验证的领域工具，MadGraph5_aMC@NLO、Pythia、Delphes用于生成对撞机模拟，MLAnalysis用于提取特征。智能体不能修改锁定配置之外的物理参数，并且所有由大语言模型生成的工件，包括参数文件、可观测量选择、算法选择和散文草稿，在执行前都记录在机器可读的阶段清单中。这些清单为SMEFT现象学研究建立了完全的可重复性和审核可追溯性。
英文摘要
We present SMEFT-Pheno-Agent, a Python workflow guided by a natural-language AI agent to perform machine-learning-assisted Standard Model Effective Field Theory (SMEFT) phenomenology at high-energy colliders. The software coordinates twelve automated execution phases spanning configuration intake, environment validation, event generation, machine-learning selection, statistical inference, and final audit. At each phase boundary, the agent interprets natural-language intent to generate runnable parameter files and adapter invocations required for subsequent execution. Once the detector-level events are written, the agent automatically proposes key kinematic observables alongside candidate machine-learning algorithms suited to the specific data structure and analysis objectives. All numerical calculations are delegated strictly to validated domain tools, with MadGraph5_aMC@NLO, Pythia, Delphes generating collider simulations, and MLAnalysis extracting features. The agent cannot modify physical parameters outside the locked configuration, and all LLM-produced artifacts, including parameter files, observable choices, algorithm selections, and prose drafts, are documented in machine-readable phase manifests prior to execution. These manifests establish complete reproducibility and audit traceability for SMEFT phenomenology studies.
