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

Title:
pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses
Authors:
Vaslin, Louis, Calvet, Samuel, Barra, Vincent, Donini, Julien
Contributors:
Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA)
Source:
SciPost Physics Codebases. :15-15
Publisher Information:
CCSD, 2023.
Publication Year:
2023
Collection:
collection:IN2P3
collection:PRES_CLERMONT
collection:LPC-CLERMONT
collection:CNRS
collection:LIMOS
collection:ACL-SF
collection:CLERMONT-AUVERGNE-INP
Subject Terms:
new particle, performance, programming, statistical analysis, data analysis method, [INFO.INFO-LG]Computer Science [cs], Machine Learning [cs.LG], [PHYS.HEXP]Physics [physics], High Energy Physics - Experiment [hep-ex]
Original Identifier:
ARXIV: 2208.14760
INSPIRE: 2144128
HAL: hal-03774091
Document Type:
Zeitschrift article<br />Journal articles
Language:
English
ISSN:
2949-804X
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2208.14760; info:eu-repo/semantics/altIdentifier/doi/10.21468/SciPostPhysCodeb.15
DOI:
10.21468/SciPostPhysCodeb.15
Access URL:
https://hal.science/hal-03774091
https://hal.science/hal-03774091v1/document
https://hal.science/hal-03774091v1/file/pdf.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.03774091v1
Database:
HAL

Weitere Informationen

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.