MLAnalysis: An open-source program for high energy physics analyses

Guo, Yu-Chen; Feng, Fan; Di, An; Lu, Shi-Qi; Yang, Ji-Chong

doi:10.1016/j.cpc.2023.108957

Computer Physics Communications

2024

DOI: 10.1016/j.cpc.2023.108957

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MLAnalysis: An open-source program for high energy physics analyses

Yu-Chen Guo,

Fan Feng,

An Di

et al.

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2024

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Cited by 4 publications

References 37 publications

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Searching for gluon quartic gauge couplings at muon colliders using the autoencoder

Zhang,

Wang,

Yang

2024

Phys. Rev. D

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One of the difficulties one has to face in the future phenomenological studies of new physics (NP), is the need to deal with increasing amounts of data. It is therefore increasingly important to improve the efficiency in the phenomenological study of NP. Whether it is the use of the Standard Model effective field theory (SMEFT), the use of machine learning (ML) algorithms, or the use of quantum computing, all are means of improving the efficiency. In this paper, we use a ML algorithm, the autoencoder (AE), to study the dimension-8 operators in the SMEFT which contribute to the gluon quartic gauge couplings (gQGCs) at muon colliders. The AE is one of the ML algorithms that has the potential to be accelerated by the quantum computing. It is found that the AE-based anomaly detection algorithm can be used as event selection strategy to study the gQGCs at the muon colliders, and is effective compared with traditional event selection strategies. Published by the American Physical Society 2024

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Searching for gluon quartic gauge couplings at muon colliders using the autoencoder

Zhang,

Wang,

Yang

2024

Phys. Rev. D

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Intrinsic geometry of collider observations and forman Ricci curvature

Beuria

2024

Phys. Rev. D

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We study the global and local topological properties of multilepton patterns reconstructed at the detectors. We investigate the sensitivity of Forman Ricci curvature distributions and persistent homology features to kinematic cuts, integrated luminosity, and scales of maximum filtration. We find that these topological properties are efficient enough in discriminating the beyond Standard Model (BSM) scenarios from the SM background, when the BSM scenarios possess a massive invisible particle. We also find that the topological properties exhibit a scaling behavior with integrated luminosity. This exploratory study suggests that the topological features can potentially supplement the traditional cut-and-count analyses in search of new physics. Published by the American Physical Society 2024

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Optimize the event selection strategy to study the anomalous quartic gauge couplings at muon colliders using the support vector machine and quantum support vector machine

Zhang,

Guo,

Yang

2024

Eur. Phys. J. C

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The search of the new physics (NP) beyond the Standard Model is one of the most important topics in current high energy physics. With the increasing luminosities at the colliders, the search for NP signals requires the analysis of more and more data, and the efficiency in data processing becomes particularly important. As a machine learning algorithm, support vector machine (SVM) is expected to to be useful in the search of NP. Meanwhile, the quantum computing has the potential to offer huge advantages when dealing with large amounts of data, which suggests that quantum SVM (QSVM) is a potential tool in future phenomenological studies of the NP. How to use SVM and QSVM to optimize event selection strategies to search for NP signals are studied in this paper. Taking the tri-photon process at a muon collider as an example, it can be shown that the event selection strategies optimized by the SVM and QSVM are effective in the search of the dimension-8 operators contributing to the anomalous quartic gauge couplings.

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MLAnalysis: An open-source program for high energy physics analyses

Cited by 4 publications

References 37 publications

Searching for gluon quartic gauge couplings at muon colliders using the autoencoder

Searching for gluon quartic gauge couplings at muon colliders using the autoencoder

Intrinsic geometry of collider observations and forman Ricci curvature

Optimize the event selection strategy to study the anomalous quartic gauge couplings at muon colliders using the support vector machine and quantum support vector machine

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