利用大型拉格朗日模型搜索BSM实验特征

中文摘要 AI 辅助

超越标准模型（BSM）物理的搜索通常不受理论描述供应的限制，而是受缺乏区分性实验观测的限制。暗物质就是一个典型例子，压倒性的引力证据在区分广阔理论空间中的模型方面作用有限。探索可测试模型特征的空间可能有助于识别被忽视的实验观测量，并表明未来实验的效用。一个挑战是设计在文献之外搜索模型特征的方法。我们的主要贡献是hAIthem，一个将强化学习（RL）的自我引导探索与LLM的广泛文献知识相结合的框架。我们构建了一个RL智能体，通过玩“战舰”风格的游戏对抗一系列唯象学工具，学习找出理论高维参数空间中未被某些约束子集排除的部分。该智能体被构建为大型拉格朗日模型（LLaM），一个自回归变换器，读取标记化的拉格朗日量，在大规模上预训练（此处使用约10,000个拉格朗日量的约10亿个令牌），并在实时环境中进行微调。该框架随后构建一个决策树，使用已建立工具计算的观测量来分离RL发现的区域，并将剩余简并区域传递给一组LLM智能体，它们竞争产生现实的特征。在此概念验证中，RL搜索优于进化算法基线，找到更多具有更大物理多样性的可行区域。在单暗标量多重态模型的受限空间中，我们发现hAIthem提出了先前研究观测量的有趣组合，例如将晕无关运动学比率应用于古探测器。

英文摘要

The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating experimental observations. A case in point is dark matter, where the overwhelming gravitational evidence only goes so far in distinguishing between models within a vast theory space. Exploring the space of testable model signatures may help identify overlooked experimental observables and indicate the utility of future experiments. A challenge is designing a search through model signatures outside what is found in the literature. Our primary contribution is hAIthem, a framework that combines the self-guided exploration of reinforcement learning (RL) with the broad literature-derived knowledge of LLMs. We build an RL agent that learns to find which portions of a theory's high-dimensional parameter space are not excluded under some subset of constraints by playing a Battleship-style "game" against a suite of phenomenology tools. The agent is built as a Large Lagrangian Model (LLaM), an autoregressive transformer that reads a tokenized Lagrangian, is pretrained at scale (here on ~1 billion tokens from ~10,000 Lagrangians), and is fine-tuned in a live environment. The framework then constructs a decision tree that separates RL-found regions using observables computed with established tools, and passes the remaining degenerate regions to a set of LLM agents that compete to produce realistic signatures. In this proof of concept, RL-search outperforms an evolutionary-algorithm baseline, finding more viable regions with greater physical diversity. In a restricted space of single dark scalar multiplet models, we find that hAIthem proposes interesting combinations of previously studied observables, such as the application of a halo-independent kinematic ratio to paleo-detectors.

发表机构

• University of Toronto（多伦多大学）
• Vector Institute（向量研究所）
• Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
• University of California, Irvine（加州大学欧文分校）
• Halluminate
• Georgia Institute of Technology（佐治亚理工学院）

机构由 AI 辅助整理，请以论文原文为准。

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47 pages, 27 Figures