强化学习助力寻找新物理
Searching for New Physics with Reinforcement Learning
物理学家用强化学习自动寻找解释异常的标准模型算子，比人类分析更全面高效。
研究人员提出一种强化学习方法，用于寻找标准模型有效场理论(SMEFT)算子来解释粒子物理异常。该方法在CDF W质量异常测试中复现并改进了已知结果。面对多个复杂异常情况，该方法仍能找到解释数据的SMEFT算子。该研究为高效寻找新物理提供了新途径。
Searching for New Physics with Reinforcement Learning
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.