论文标题
在边缘寻找颠簸
Hunting for bumps in the margins
论文作者
论文摘要
数据驱动的建模对于对撞机实验的许多分析至关重要，但是，物理特性的推断会遵守模型拟合程序的细节。这项工作将数据建模和信号提取的原则性贝叶斯图片带到了常见的对撞机物理方案中。首先，基于边际可能性的方法用于提出更有原则的背景过程结构，系统地探索了各种候选形状。其次，将图片扩展为提出边际可能性，作为粒子物理学异常检测挑战的有用工具。该建议提供了对精确背景模型确定的洞察力，并展示了一种灵活的方法，可以将信号确定扩展到简单的凸起狩猎之外。
Data driven modelling is vital to many analyses at collider experiments, however the derived inference of physical properties becomes subject to details of the model fitting procedure. This work brings a principled Bayesian picture, based on the marginal likelihood, of both data modelling and signal extraction to a common collider physics scenario. First the marginal likelihood based method is used to propose a more principled construction of the background process, systematically exploring a variety of candidate shapes. Second the picture is extended to propose the marginal likelihood as a useful tool for anomaly detection challenges in particle physics. This proposal offers insight into both precise background model determination and demonstrates a flexible method to extend signal determination beyond a simple bump hunt.
