发表机构
University of Massachusetts Amherst; University of Wisconsin–Madison（马萨诸塞大学阿默斯特分校; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出构造有限加权参考样本以近似Asimov数据集，用于神经模拟推断的频率论分析，仅需256个事件即可重现两百万事件的结果，并强调需验证学习比值的准确性。
AI 中文摘要
在基于神经模拟推断的频率论分析中，预期灵敏度计算通常需要大量模拟样本来近似无分箱的Asimov数据集。我们为基于学习密度比的分析构造了一个有限加权参考样本，使得生成参数全局最大化加权似然。一个基于高能物理模型松散构建的五维观测空间玩具示例表明，仅由256个加权参考事件组成的Asimov数据集能够紧密重现使用两百万个模拟参考事件获得的预期检验统计量扫描。所得检验统计量分布也被证明与独立的基于模拟器的伪实验一致。我们注意到，这种与模拟器的一致性取决于学习比值的准确性，必须明确验证。
英文摘要
Expected sensitivity calculations in frequentist analysis using neural simulation-based inference often require large simulated samples to approximate an unbinned Asimov dataset. We construct a finite weighted reference sample, for analyses based on learned density ratios, for which the generating parameters globally maximize the weighted likelihood. A toy example with five-dimensional observable space based loosely on high-energy physics models shows that an Asimov dataset consisting of as few as 256 weighted reference events closely reproduce the expected test statistic scans obtained with two million simulated reference events. The resulting test statistic distributions are also shown to agree with independent simulator-based pseudo-experiments. We note that this agreement with the simulator depends on the accuracy of the learned ratios and must be validated explicitly.
Comments5 pages, 2 figures
