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 4σ.

我们引入了一种称为 D rapes的新技术，以提高大型强子对撞机寻找新物理的灵敏度。通过在边带数据上训练扩散模型，我们展示了如何直接从噪声或通过将扩散过程部分应用于现有数据来生成信号区域的背景模板。在部分扩散情况下，可以从边带区域提取数据，对新的目标条件值执行逆扩散，或者从信号区域提取数据，保留定义信号区域的条件属性上的分布。我们将该技术应用于使用 LHCO di-jet 数据集寻找共振，并使用高级输入特征实现背景模板生成的最先进性能。我们还展示了如何将 D油菜应用于具有射流成分的低水平输入，从而减少模型对输入可观测值选择的依赖。使用射流成分，我们可以进一步提高对信号过程的灵敏度，但观察到性能损失，其中应用任何选择之前的信号显着性低于 4 σ。