Title: Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference Show Chinese title

Title: 用神经网络辅助的基于模拟的推断探索BSM参数空间

Abstract: Some of the issues that make sampling parameter spaces of various beyond the Standard Model (BSM) scenarios computationally expensive are the high dimensionality of the input parameter space, complex likelihoods, and stringent experimental constraints. In this work, we explore likelihood-free approaches, leveraging neural network-aided Simulation-Based Inference (SBI) to alleviate this issue. We focus on three amortized SBI methods: Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE) and perform a comparative analysis through the validation test known as the \textit{ Test of Accuracy with Random Points} (TARP), as well as through posterior sample efficiency and computational time. As an example, we focus on the scalar sector of the phenomenological minimal supersymmetric SM (pMSSM) and observe that the NPE method outperforms the others and generates correct posterior distributions of the parameters with a minimal number of samples. The efficacy of this framework will be more evident with additional experimental data, especially for high dimensional parameter space. Show Chinese abstract

Abstract: 一些使各种超出标准模型（BSM）情景的参数空间采样在计算上昂贵的问题包括输入参数空间的高维度、复杂的似然函数以及严格的实验约束。 在本工作中，我们探索了无需似然的方法，利用神经网络辅助的基于模拟的推断（SBI）来缓解这一问题。 我们重点关注三种摊销SBI方法：神经后验估计（NPE）、神经似然估计（NLE）和神经比率估计（NRE），并通过已知的验证测试\textit{精度测试与随机点}（TARP）以及后验样本效率和计算时间进行比较分析。 作为示例，我们关注现象学最小超对称标准模型（pMSSM）的标量部分，并观察到NPE方法优于其他方法，并且在最少样本数量下生成参数的正确后验分布。 随着更多实验数据的加入，该框架的有效性将更加明显，尤其是在高维参数空间的情况下。