Detecting anomalous quartic gauge couplings using the isolation forest machine learning algorithm

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.