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arXiv 2203.13403hep-ph
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基于模拟的有效理论空间采样推断:应用于反常μ子(g-2)的超对称解释

Simulation Based Inference for Efficient Theory Space Sampling: an Application to Supersymmetric Explanations of the Anomalous Muon (g-2)

Logan Morrison, Stefano Profumo, Nolan Smyth, John Tamanas

摘要:

为了最小化样本模型评估的数量,我们提出并研究了利用(顺序)版本的似然证据比神经estimation.We应用我们的算法的超对称解释的异常μ子磁偶极矩的背景下,标准模型的现象学最小超对称扩展,并恢复非平凡的模型在实验约束的理论空间。最后,我们总结了这些算法在未来研究中的进一步潜在可能的用途。

英文摘要:

For the purpose of minimizing the number of sample model evaluations, we propose and study algorithms that utilize (sequential) versions of likelihood-to-evidence ratio neural estimation.We apply our algorithms to a supersymmetric interpretation of the anomalous muon magnetic dipole moment in the context of a phenomenological minimal supersymmetric extension of the standard model, and recover non-trivial models in an experimentally-constrained theory space. Finally we summarize further potential possible uses of these algorithms in future studies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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