SciPost Phys. Core 8, 091 (2025) ·
published 11 December 2025
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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.
