Search for anomalous quartic gauge couplings in the process ${\mu }^{+}{\mu }^{-}\to \bar{\nu }\nu \gamma \gamma$ with a nested local outlier factor

Abstract

In recent years, with the increasing luminosities of colliders, handling the growing amount of data has become a major challenge for future new physics~(NP) phenomenological research. To improve efficiency, machine learning algorithms have been introduced into the field of high-energy physics. As a machine learning algorithm, the local outlier factor~(LOF), and the nested LOF~(NLOF) are potential tools for NP phenomenological studies. In this work, the possibility of searching for the signals of anomalous quartic gauge couplings~(aQGCs) at muon colliders using the NLOF is investigated. Taking the process ${\mu }^{+}{\mu }^{-}\to \nu \bar{\nu }\gamma \gamma$ as an example, the signals of dimension-8 aQGCs are studied, expected coefficient constraints are presented. The event selection strategy uses unsupervised anomaly scores, with supervised optimization for EFT sensitivity. The NLOF algorithm is shown to outperform the k-means based anomaly detection methods, and a traditional counterpart.

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Cited by in corpus (1)

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