利用基于模拟的推断学习暗物质直接探测中的天体物理不确定性
Learning Astrophysical Uncertainties in Dark Matter Direct Detection with Simulation-Based Inference
浏览论文内容
中文总结 AI 辅助
本研究通过神经比率估计方法处理暗物质直接探测中的天体物理不确定性，在XENONnT实验中实现隐式边缘化，并验证了其在错误设定下的稳健性及与预期灵敏度的定量一致性。
中文摘要 AI 辅助
我们提出了一项神经比率估计（NRE）的概念验证研究——这是一种基于模拟的推断算法——以XENONnT实验为例应用于WIMP直接探测。我们在模拟的参数-观测对上训练一个二元分类器，以近似WIMP质量$m_\chi$和自旋无关质子耦合$c_p$的似然比，使用由fuse探测器模拟产生的真实闪烁和电离信号。我们证明，通过在训练数据中包含多个晕模型，来自晕模型变化的天体物理不确定性被隐式边缘化，且不增加额外的推断成本。使用极端的$\pm5\sigma$标准晕模型变化，我们表明在组合数据集上训练即使在严重错误设定下也能恢复良好校准的后验，而更现实的晕模型产生几乎不可区分的结果。信号加背景分类器给出的排除限在定性上以及（在考虑频率学派和贝叶斯方法之间的差异后）定量上与XENONnT预期灵敏度一致。这些结果确立了NRE作为轮廓似然比方法的可扩展补充，特别是在实验深入探测中微子雾时具有优势。
英文摘要
We present a proof-of-concept study of neural ratio estimation (NRE) - a simulation-based inference algorithm - applied to WIMP direct detection using the XENONnT experiment as an example. A binary classifier is trained on simulated parameter-observation pairs to approximate the likelihood ratio for the WIMP mass $m_χ$ and spin-independent proton coupling $c_p$, using realistic scintillation and ionization signals produced by the fuse detector simulation. We demonstrate that astrophysical uncertainties from halo model variations are marginalized over implicitly and at no additional inference cost by including multiple halo models in the training data. Using extreme $\pm5σ$ Standard Halo Model variations, we show that training on a combined dataset recovers well-calibrated posteriors even under severe misspecification, while more realistic halo models produce nearly indistinguishable results. The signal-plus-background classifier yields an exclusion limit that is both qualitatively and (after accounting for differences between frequentist and Bayesian approaches) quantitatively consistent with the XENONnT expected sensitivity. These results establish NRE as a scalable complement to the profile likelihood ratio approach, with particular advantages as experiments probe deeper into the neutrino fog.
发表机构
- Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。
