Data-driven methods of background estimations are often used to obtain more reliable descriptions of backgrounds. In hadron collider experiments, data-driven techniques are used to estimate backgrounds due to multi-jet events, which are difficult to model accurately. In this article, we propose an improvement on one of the most widely used data-driven methods in the hadron collision environment, the “ABCD” method of extrapolation. We describe the mathematical background behind the data-driven methods and extend the idea to propose improved general methods.

A preprint version of the article is available at ArXiv.

中文翻译：

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高能物理中数据驱动背景估计的改进外推方法

背景估计的数据驱动方法通常用于获得更可靠的背景描述。在强子对撞机实验中，数据驱动技术用于估计多喷流事件引起的背景，这很难准确建模。在本文中，我们提出了对强子碰撞环境中使用最广泛的数据驱动方法之一的改进，即“ABCD”外推方法。我们描述了数据驱动方法背后的数学背景，并扩展了这一想法以提出改进的通用方法。

该文章的预印版可在 ArXiv 上获得。