标题： 推断相关联的分布：助推顶喷流 显示英文标题

标题： Inferring correlated distributions: boosted top jets

摘要： 改善对信号区信号和背景分布的理解是提升对撞物理中任何分析质量的关键所在。 这通常是一项艰巨的任务，因为——除其他原因外——信号与背景在信号区内难以区分，模拟可能达到可靠性极限，如果它们需要建模非微扰 QCD 的话，并且分布在多维空间中，很多时候各维度之间可能存在相关性。 贝叶斯密度估计是一种利用先验知识和数据关联来有效提取信号区数据信息的技术。 在这项工作中，我们将之前关于 collider 物理中有意义的无监督信号提取的数据驱动混合模型研究进一步扩展，以纳入特征之间的相关性。 使用 top 和 QCD 喷注的标准数据集，我们展示了尽管模拟器存在预期偏差，但可以通过它们向推理模型注入足够的归纳细微差别（特别是在先验方面），然后由数据进行修正并估算各类别内特征间的真正相关分布。 我们将带相关性和不带相关性的模型进行对比，以展示信号提取对相关性加入的敏感程度，并通过监督和无监督指标量化相关性加入带来的改进。 显示英文摘要

摘要： Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because -- among others -- signal and backgrounds are hard to discriminate in signal-region, simulations may reach a limit of reliability if they need to model non-perturbative QCD, and distributions are multi-dimensional and many times may be correlated within each class. Bayesian density estimation is a technique that leverages prior knowledge and data correlations to effectively extract information from data in signal-region. In this work we extend previous works on data-driven mixture models for meaningful unsupervised signal extraction in collider physics to incorporate correlations between features. Using a standard dataset of top and QCD jets, we show how simulators, despite having an expected bias, can be used to inject sufficient inductive nuance into an inference model in terms of priors to then be corrected by data and estimate the true correlated distributions between features within each class. We compare the model with and without correlations to show how the signal extraction is sensitive to their inclusion and we quantify the improvement due to the inclusion of correlations using both supervised and unsupervised metrics.