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

TL;DR

pyBumpHunter is a new Python implementation of the BumpHunter algorithm, enabling model-independent searches for localized deviations in high energy physics data with improved features and performance.

Contribution

It introduces the first public Python implementation of BumpHunter integrated into Scikit-HEP, with enhancements and additional features for high energy physics analyses.

Findings

Demonstrated the performance of new features

Validated the algorithm's effectiveness in detecting deviations

Showcased integration with Scikit-HEP

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.