pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses

Abstract

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.

14 pages, 9 figures

References in corpus (4)

• Array Programming with NumPy
• Choosing Between Methods of Combining p-values
• On hypothesis testing, trials factor, hypertests and the BumpHunter
• Fitting the BumpHunter test statistic distribution and global p-value estimation

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