用强化学习寻找新物理
Searching for New Physics with Reinforcement Learning
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中文总结 AI 辅助
本文提出一种强化学习方法，用于在标准模型有效场论中自动搜索能解释异常（如CDF W质量异常）的算子，无需人工唯象直觉，可高效探索完整算子空间。
中文摘要 AI 辅助
寻找新物理（NP）是当今粒子物理学最重要的问题。研究“异常”，即低能观测量与标准模型（SM）预言不一致的测量值，是一种强有力的搜索策略。标准模型有效场论（SMEFT）为参数化新物理提供了一个通用的模型无关框架；自然要尝试找到能够解释这些异常的SMEFT算子。这是一项具有挑战性的任务，因为（i）SMEFT算子的数量巨大，（ii）在圈图水平上，算子之间存在非常复杂的关联。人类的分析通常依赖唯象直觉来决定哪些算子是相关的。这往往带有偏见，并且没有探索完整的SMEFT算子空间。有趣的是，强化学习（RL）技术在需要决策以实现目标的任务中表现出色。在本文中，我们介绍了一种RL方法，可用于找到解释任何异常的SMEFT算子。我们在CDF W质量异常上进行了测试，结果表明它能够重现（并改进）已知结果。然后我们考虑了一个涉及多个异常的更为复杂的情况，并表明即使在这种情况下，该方法也能找到解释数据的SMEFT算子。因此，我们的RL方法可以有效地在SMEFT层面上搜索新物理。
英文摘要
Finding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i.e., measurements of low-energy observables whose values disagree with the predictions of the Standard Model (SM), is a powerful search strategy. The SM Effective Field Theory (SMEFT) provides a general model-independent framework for parameterizing NP; it is natural to try to find the SMEFT operator(s) that can explain such anomalies. This is a challenging task because (i) the number of SMEFT operators is enormous, and (ii) at loop level there are very complicated correlations among the operators. Analyses by humans typically rely on phenomenological intuition to decide which operators are relevant. This is often biased and does not explore the complete SMEFT operator space. Interestingly, reinforcement learning (RL) techniques excel at tasks that require decision making to achieve their goals. In this paper, we introduce an RL method that can be used to find the SMEFT operators that explain any anomalies. We test it on the CDF $W$-mass anomaly, and show that it reproduces (and improves upon) known results. We then consider a far more complicated situation with multiple anomalies and show that, even here, this method is able to find the SMEFT operators that explain the data. Our RL method can therefore be used to efficiently search for NP at the level of SMEFT.
发表机构
- Université de Montréal（蒙特利尔大学）
机构由 AI 辅助整理，请以论文原文为准。
