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

Louis Vaslin (1) , Samuel Calvet (1) , Vincent Barra (2) , Julien Donini (1)

1 LPC - Laboratoire de Physique de Clermont
2 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes

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.

Mots clés: new particle; performance; programming; statistical analysis; data analysis method

Domaines: Apprentissage [cs.LG]; Physique des Hautes Energies - Expérience [hep-ex]

Louis Vaslin, Samuel Calvet, Vincent Barra, Julien Donini. pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses. 2024. ⟨hal-03774091⟩