基于数据驱动的神经元自回归流背景分布估计
Data-driven Estimation of Background Distribution through Neural Autoregressive Flows
摘要：
我们报告了一种使用神经自回归流（NAF）的通用和自动数据驱动的背景分布形状估计方法，这是深度生成学习方法之一。数据驱动的背景估计是不可或缺的许多分析，涉及复杂的最终状态，可靠的预测是不可用的。NAF允许我们从有限数量的可逆一维函数中构造出在多维空间上操作的一般双射变换。由于数据驱动的背景估计可以表示为变换，因此该方法简单、通用，非常适合应用于数据驱动的背景估计。在数据驱动的背景估计中，目标是导出适当的变换并将外推变换应用于感兴趣区域。在ABCDnn方法中，我们可以让NAF通过多个控制区域来学习变换对控制变量的依赖性。我们证明，通过ABCDnn方法的预测是类似的最佳情况下，而具有较小的统计不确定性。
英文摘要：
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.
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