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

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 small in number 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 constrain the coefficients of aQGCs. As a machine learning algorithm, the IF algorithm shows good prospects in future studies of NP.