Search for anomalous quartic gauge couplings in the process $μ^+μ^-\to \barννγγ$ with a nested local outlier factor

TL;DR

This work investigates searching for anomalous quartic gauge couplings (aQGCs) at muon colliders using a nested local outlier factor (NLOF) anomaly-detection framework. It leverages SMEFT dimension-8 operators $O_{M_i}$ and $O_{T_j}$ in the process $\mu^+\mu^-\to \nu\bar{\nu}\gamma\gamma$ to probe aQGCs within unitarity bounds, employing an unsupervised anomaly-score strategy with supervised EFT-sensitivity tuning. The study shows that NLOF substantially improves sensitivity over LOF and other anomaly-detection methods, providing projected constraints on operator coefficients at $\sqrt{s}=3$ TeV and $10$ TeV with high luminosities. The approach is built on a five-parameter photon-based feature space and a NAD framework that uses SM as the reference, with results suggesting strong potential for model-agnostic NP searches at future high-luminosity colliders. The findings indicate that NLOF is a robust, scalable method for high-energy NP searches and may be extendable with quantum kernels in the future.

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 $μ^+μ^-\to ν\barνγγ$ 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.

Figures (9)

• Figure 1: Typical Feynman diagrams for signal events (the upper panels) and background events (the lower panels).
• Figure 2: Fitted cross sections after cut using the LOF (the left panel) and the NLOF (the right panel) algorithms at $\sqrt{s} = 10 \;\rm{TeV}$ for $O_{T_{1} }$.
• Figure 3: Expected constraint on the coefficient $f_{T_1}/\Lambda ^4$ as a function of $\Delta a_{th}$ for the NLOF at $\sqrt{s} = 10 \;\rm{TeV}$.
• Figure 4: Fitted cross sections after cut at $\sqrt{s}=3$ TeV for $O_{M_{2,3,4} }$ (the left panel) and $O_{T_{0,1,2,5,6,7} }$ (the right panel).
• Figure 5: Fitted cross sections after cut at $\sqrt{s}=3$ TeV for $O_{T_{0,1,2} }$ (the left panel) and $O_{T_{5,6,7} }$ (the right panel).