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

TL;DR

This paper applies the isolation forest machine learning algorithm to identify and constrain anomalous quartic gauge couplings, demonstrating its effectiveness in detecting rare, kinematically unusual events indicative of new physics beyond the Standard Model.

Contribution

The study introduces the use of the isolation forest algorithm for model-independent detection and constraint of anomalous quartic gauge couplings in high energy physics.

Findings

Isolation forest effectively identifies anomalous events.

The method can constrain coefficients of aQGCs.

Machine learning shows promise for future new physics searches.

Abstract

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.