论文标题
pybumphunter：用于高能物理分析的Python中的独立模型撞击工具
pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses
论文作者
论文摘要
Bumphunter算法广泛用于在高能物理分析中寻找新粒子。该算法提供了评估观察到的数据中局部偏差的局部和全局p值的优点，而无需对假定信号做任何假设。 Python编程语言的日益普及激发了该算法在Python（称为Pybumphunter）进行新的公共实施的开发，以及一些改进和其他功能。这是Bumphunter算法的第一个公共实施，将添加到Scikit-Hep中。本文详细介绍了Bumphunter算法以及本实现中提出的所有功能。所有这些功能都已进行了测试，以证明其行为和表现。
The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.
