The Higgs Machine Learning Challenge

Claire Adam-Bourdarios 1 G. Cowan 2 Cecile Germain-Renaud 3, 4 Isabelle Guyon 5, 3, 4 Balázs Kégl 1 D. Rousseau 1
4 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to τ+τ– together with background events were made available to the public through the website of the data science organization Kaggle (kaggle.com). Participants attempted to identify the search region in a space of 30 kinematic variables that would maximize the expected discovery significance of the signal process. One of the primary goals of the Challenge was to promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2,000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, particularly from ML into HEP, is currently underway.
Type de document :
Article dans une revue
Journal of Physics: Conference Series, IOP Publishing, 2015, 664 (7), 〈10.1088/1742-6596/664/7/072015〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01745998
Contributeur : Cecile Germain <>
Soumis le : mercredi 28 mars 2018 - 19:06:37
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

Lien texte intégral

Identifiants

Citation

Claire Adam-Bourdarios, G. Cowan, Cecile Germain-Renaud, Isabelle Guyon, Balázs Kégl, et al.. The Higgs Machine Learning Challenge. Journal of Physics: Conference Series, IOP Publishing, 2015, 664 (7), 〈10.1088/1742-6596/664/7/072015〉. 〈hal-01745998〉

Partager

Métriques

Consultations de la notice

214