We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motivated by the search for the production of pairs of Higgs bosons decaying into four bottom quarks. A number of other physical processes, known as background, also share the same final state. The data arising in this problem is therefore a mixture of unlabeled background and signal events, and the primary aim of the analysis is to determine whether the proportion of unlabeled signal events is nonzero. A challenging but necessary first step is to estimate the distribution of background events. Past work in this area has determined regions of the space of collider events where signal is unlikely to appear, and where the background distribution is therefore identifiable. The background distribution can be estimated in these regions, and extrapolated into the region of primary interest using transfer learning with a multivariate classifier. We build upon this existing approach in two ways. First, we revisit this method by developing a powerful new classifier architecture tailored to collider data. Second, we develop a new method for background estimation, based on the optimal transport problem, which relies on modeling assumptions distinct from earlier work. These two methods can serve as cross-checks for each other in particle physics analyses, due to the complementarity of their underlying assumptions. We compare their performance on simulated double Higgs boson data.

翻译：我们研究数据驱动背景估计问题，该问题源于大型强子对撞机中标准模型预测的物理信号搜索。本研究的工作动机源于对希格斯玻色子对产生并衰变为四个底夸克过程的搜索。其他一些称为背景的物理过程也共享相同的末态。因此，该问题中产生的数据是未标记背景事件与信号事件的混合体，分析的主要目标是确定未标记信号事件的比例是否非零。一个具有挑战性但必要的第一步是估计背景事件的分布。该领域先前的研究已确定对撞机事件空间中信号不太可能出现的区域，在这些区域中背景分布是可识别的。背景分布可在这些区域中估计，并通过使用多变量分类器的迁移学习外推至主要感兴趣区域。我们在以下两个方面对此现有方法进行改进：首先，我们重新审视该方法，开发了一种专为对撞机数据设计的强大新型分类器架构；其次，我们基于最优输运问题开发了一种新的背景估计方法，该方法的建模假设与先前工作不同。由于这两种方法的基本假设具有互补性，它们可以在粒子物理分析中相互进行交叉验证。我们在模拟的双希格斯玻色子数据上比较了它们的性能。