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The Higgs boson machine learning challenge

Claire Adam-Bourdarios 1 Glen Cowan 2 Cécile Germain 3, 4, 5 Isabelle Guyon 6 Balázs Kégl 3, 1, 5 David Rousseau 1
3 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 Boson Machine Learning Challenge (HiggsML or the Challenge for short) was organized to promote collaboration between high energy physicists and data scientists. The ATLAS experiment at CERN provided simulated data that has been used by physi- cists in a search for the Higgs boson. The Challenge was organized by a small group of ATLAS physicists and data scientists. It was hosted by Kaggle at https://www.kaggle. com/c/higgs-boson; the challenge data is now available on collection/ATLAS-Higgs-Challenge-2014. This paper provides the physics background and explains the challenge setting, the challenge design, and analyzes its results.
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Submitted on : Wednesday, October 7, 2015 - 12:50:35 PM
Last modification on : Wednesday, October 14, 2020 - 3:49:42 AM
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  • HAL Id : hal-01208587, version 1



Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, et al.. The Higgs boson machine learning challenge. NIPS 2014 Workshop on High-energy Physics and Machine Learning, Dec 2014, Montreal, Canada. pp.37. ⟨hal-01208587⟩



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