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Search for anomalous quartic gauge couplings in the process $\mu^+\mu^-\to \bar{\nu}\nu\gamma\gamma$ with a nested local outlier factor
by Ke-Xin Chen, Yu-Chen Guo, Ji-Chong Yang
This is not the latest submitted version.
Submission summary
| Authors (as registered SciPost users): | Ji-Chong Yang |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202507_00021v1 (pdf) |
| Code repository: | https://gitee.com/NBAlexis/CutExperiment |
| Data repository: | https://www.modelscope.cn/datasets/nbalexis/Collision_events_of_aavv_final_states_with_aQGCs_at_muon_colliders |
| Date submitted: | July 7, 2025, 7:45 p.m. |
| Submitted by: | Ji-Chong Yang |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
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 NLOF algorithm are shown to outperform the k-means based anomaly detection methods, and a tradition counterpart.
Author indications on fulfilling journal expectations
- Address an important (set of) problem(s) in the field using appropriate methods with an above-the-norm degree of originality
- Detail one or more new research results significantly advancing current knowledge and understanding of the field.
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Strengths
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The physics case is relevant: muon colliders are increasingly discussed as next-generation machines, and aQGCs at dimension-8 are a natural test ground.
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NLOF is a new and fresh idea in this context. The paper reasonably explains how LOF and NLOF work, which is useful for readers not familiar with these algorithms.
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The results are well illustrated, with fitted cross sections and clear comparisons between different methods.
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The focus on dimension-8 operators (disentangling quartic from triple gauge couplings) is well motivated.
Weaknesses
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Detector realism is too limited. The study uses a simple Delphes card with no detailed discussion of photon identification efficiencies or systematic uncertainties. This makes the quoted sensitivities likely too optimistic.
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Benchmarking is weak. The paper compares NLOF mainly to LOF, k-means, and a simple cut-based strategy. However, ATLAS and CMS have already explored more advanced unsupervised methods such as autoencoders, and normalizing flows. Without at least discussing these, the paper risks overstating the originality of NLOF algorithm.
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SMEFT validity is not fully addressed. The authors include unitarity bounds but do not discuss the EFT breakdown at 10 TeV in detail. Some quoted bounds are so tight that it is questionable whether SMEFT is reliable in that region.
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Hyperparameter tuning. The NLOF thresholds and neighborhood size are chosen somewhat ad hoc, with no systematic scan. This raises questions about reproducibility and robustness.
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How could NLOF be implemented efficiently for realistic event counts. Is it scalable to many events ?
Report
Requested changes
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Provide a more realistic discussion of detector effects and systematics, especially muon-collider specific challenges.
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Compare NLOF (at least in discussion) with SOTA anomaly detection methods already studied and used by ATLAS/CMS (autoencoders, flows, weak supervision).
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Add a robustness check: how do results change when varying k, thresholds, or feature choices?
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Discuss EFT validity at 10 TeV more carefully, including where unitarity bounds suggest SMEFT may not be trustworthy.
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Comment on scalability of NLOF.
Recommendation
Ask for major revision