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 customized residual neural network which is tailored to the structure and symmetries of 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：我们研究了在大型强子对撞机中寻找标准模型预测的物理信号时出现的数据驱动背景估计问题。本研究受寻找衰变为四个底夸克的双希格斯玻色子对产生过程的启发。若干被称为背景的其他物理过程也具有相同的末态。因此，该问题中的数据是未标记的背景事件与信号事件的混合体，分析的主要目标是判断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初步步骤是估计背景事件的分布。该领域的已有工作确定了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进行估计，并借助多元分类器的迁移学习外推至主要关注区域。我们在现有方法基础上进行了两方面的拓展：首先，我们通过开发一个针对对撞机数据结构与对称性定制的残差神经网络，重新审视了这一方法；其次，我们基于最优输运问题提出了一种新的背景估计方法，其建模假设与早期工作存在本质差异。由于两种方法底层假设的互补性，它们可在粒子物理分析中互为验证。我们在模拟的双希格斯玻色子数据上比较了这两种方法的性能。