论文标题
通过神经自回旋流量进行数据驱动的背景分布估计
Data-driven Estimation of Background Distribution through Neural Autoregressive Flows
论文作者
论文摘要
我们使用神经自回归流（NAF）报告了通用和自动数据驱动的背景分布形状估计方法，该方法是深层生成学习方法之一。对于涉及可靠预测的复杂最终状态的许多分析，数据驱动的背景估计是必不可少的。 NAF允许我们构建在有限数量的可耐用一维函数的多维空间上运行的一般射击性变换。鉴于其简单性和普遍性，它非常适合在数据驱动的背景估计中应用，因为数据驱动的估计可以表示为转换。在数据驱动的背景估计中，目标是导致适当的转换并将推断转换应用于感兴趣的区域。在ABCDNN方法中，我们可以让NAF通过具有多个控制区域来学习转换对控制变量的依赖性。我们证明，通过ABCDNN方法的预测类似于最佳情况，同时具有较小的统计不确定性。
We report on a general and automatic data-driven background distribution shape estimation method using neural autoregressive flows (NAF), which is one of the deep generative learning methods. Data-driven background estimation is indispensable for many analyses involving complicated final states where reliable predictions are not available. NAF allow us to construct general bijective transformations that operate on multidimensional space, out of finite number of invertible one-dimensional functions. Given its simplicity and universality, it is well suited to the application in the data-driven background estimation, since data-driven estimations can be expressed as transformations. In a data-driven background estimation, the goal is to derive appropriate transformations and apply extrapolated transformations to the region of interest. In the ABCDnn method, we can have the NAF learn the transformations' dependence on control variables by having multiple control regions. We demonstrate that the prediction through ABCDnn method is similar to optimal case, while having smaller statistical uncertainty.
