We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background templates for the signal region can be generated either directly from noise, or by partially applying the diffusion process to existing data. In the partial diffusion case, data can be drawn from side-band regions, with the inverse diffusion performed for new target conditional values, or from the signal region, preserving the distribution over the conditional property that defines the signal region. We apply this technique to the hunt for resonances using the LHCO di-jet dataset, and achieve state-of-the-art performance for background template generation using high level input features. We also show how Drapes can be applied to low level inputs with jet constituents, reducing the model dependence on the choice of input observables. Using jet constituents we can further improve sensitivity to the signal process, but observe a loss in performance where the signal significance before applying any selection is below 4$\sigma$.

翻译：我们提出了一种名为Drapes的新技术，用于提升大型强子对撞机（LHC）上新物理搜索的灵敏度。通过利用边带数据训练扩散模型，我们展示了如何生成信号区域的背景模板：既可直接从噪声出发，也可通过将扩散过程部分应用于现有数据来实现。在部分扩散情况下，数据可从边带区域抽取，并针对新的目标条件值执行逆扩散；或从信号区域抽取，同时保留定义信号区域的条件属性分布。我们将该技术应用于基于LHCO双喷注数据集的共振搜索，在高层次输入特征条件下，实现了背景模板生成的最优性能。此外，我们展示了Drapes如何适用于包含喷注构成成分的低层次输入，从而降低模型对输入观测量的选择依赖性。利用喷注构成成分可进一步提升对信号过程的灵敏度，但在信号显著性（未施加任何筛选前）低于4σ时，我们观察到性能有所下降。