Using a nested anomaly detection machine learning algorithm to study the neutral triple gauge couplings at an \texorpdfstring{}{e+e-} collider
arXiv:2111.10543 · doi:10.1016/j.nuclphysb.2022.115735
Abstract
Anomaly detection algorithms have been proved to be useful in the search of new physics beyond the Standard Model. However, a prerequisite for using an anomaly detection algorithm is that the signal to be sought is indeed anomalous. This does not always hold true, for example when interference between new physics and the Standard Model becomes important. In this case, the search of new physics is no longer an anomaly detection. To overcome this difficulty, we propose a nested anomaly detection algorithm, which appears to be useful in the study of neutral triple gauge couplings at the CEPC, the ILC and the FCC-ee. Our approach inherits the advantages of the anomaly detection algorithm been nested, while at the same time, it is no longer an anomaly detection algorithm. As a complement to anomaly detection algorithms, it can achieve better results on problems that are no longer anomaly detection.
12 pages, 6 figures
References in corpus (12)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- FeynRules - Feynman rules made easy
- Test of lepton universality using decays
- The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
- An Effective Guide to Beyond the Standard Model Physics
- The study of neutral triple gauge couplings in the process including unitarity bounds
- Constraints on anomalous quartic gauge couplings via production at the LHC
- Detecting anomalous quartic gauge couplings using the isolation forest machine learning algorithm
- Contributions to () couplings from violating flavor changing couplings
- Extract the energy scale of anomalous scattering in the vector boson scattering process using artificial neural networks
- MLAnalysis: An open-source program for high energy physics analyses
- Fast simulation of the CEPC detector with Delphes
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- Model-independent study on the anomalous and couplings at the future muon collider
- MLAnalysis: An open-source program for high energy physics analyses
- Using k-means assistant event selection strategy to study anomalous quartic gauge couplings at muon colliders
- Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means
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- Non-diagonal contributions to vertex, polarizations and bounds on couplings
- Search for Neutral Triple Gauge Couplings with Production at Future Electron Positron Colliders
- Search for the anomalous and gauge couplings through the process with unpolarized and polarized beams
- Search for anomalous quartic gauge couplings in the process with a nested local outlier factor
- Searching for the neutral triple gauge couplings in the process at muon colliders
- Probing Neutral Triple Gauge Couplings at e- p colliders