论文标题
基于有效理论空间采样的基于仿真的推断：应用于异常穆恩（G-2）的超对称解释的应用
Simulation Based Inference for Efficient Theory Space Sampling: an Application to Supersymmetric Explanations of the Anomalous Muon (g-2)
论文作者
论文摘要
为了最大程度地减少样本模型评估的数量，我们提出并研究了利用（顺序）可能性的（顺序）可能性与证据比率神经估计的算法。我们将我们的算法应用于对异常磁极磁性偶极时刻的超对称解释，并在现场中恢复过度的模型中的模型，并恢复过多的模型。实验约束的理论空间。最后，我们总结了这些算法在未来的研究中的进一步潜在可能用途。
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.
