使用孤立森林机器学习算法探测反常四规范玻色子耦合
Detecting anomalous quartic gauge couplings using the isolation forest machine learning algorithm
AI总结：
本研究应用孤立森林机器学习算法，采用与模型无关的策略直接筛选运动学异常事件，有效识别并约束了反常四规范玻色子耦合信号，为新物理研究提供了新方法。
摘要：
寻找超出标准模型的新物理（NP）是高能物理最重要的任务之一。NP信号的共同特征是它们通常数量稀少且运动学特征不同。我们采用与模型无关的策略，通过直接挑选并研究运动学异常事件来研究NP的现象学。为此，我们应用了孤立森林（IF）算法，发现该算法在识别反常四规范玻色子耦合（aQGCs）的信号事件方面非常有效。IF算法还可用于约束aQGCs的系数。作为一种机器学习算法，IF算法在未来的NP研究中展现出良好前景。
英文摘要：
The search of new physics~(NP) beyond the Standard Model is one of the most important tasks of high energy physics. A common characteristic of the NP signals is that they are usually few and kinematically different. We use a model independent strategy to study the phenomenology of NP by directly picking out and studying the kinematically unusual events. For this purpose, the isolation forest~(IF) algorithm is applied, which is found to be efficient in identifying the signal events of the anomalous quartic gauge couplings~(aQGCs). The IF algorithm can also be used to constraint the coefficients of aQGCs. As a machine learning algorithm, the IF algorithm shows a good prospect in the future studies of NP.
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