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The Higgs Machine Learning Challenge

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 $\tau^+\tau^-$ together with background events were m...

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Detalles Bibliográficos
Autores principales: Cowan, Glen, Rousseau, David
Lenguaje:eng
Publicado: 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/664/7/072015
http://cds.cern.ch/record/2016636
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author Cowan, Glen
Rousseau, David
author_facet Cowan, Glen
Rousseau, David
author_sort Cowan, Glen
collection CERN
description 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 $\tau^+\tau^-$ together with background events were made available to the public through the website of the data science organization Kaggle (\verb=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 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.
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spelling cern-20166362022-08-10T12:55:12Zdoi:10.1088/1742-6596/664/7/072015http://cds.cern.ch/record/2016636engCowan, GlenRousseau, DavidThe Higgs Machine Learning ChallengeParticle Physics - ExperimentThe 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 $\tau^+\tau^-$ together with background events were made available to the public through the website of the data science organization Kaggle (\verb=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 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.ATL-SOFT-PROC-2015-044oai:cds.cern.ch:20166362015-05-17
spellingShingle Particle Physics - Experiment
Cowan, Glen
Rousseau, David
The Higgs Machine Learning Challenge
title The Higgs Machine Learning Challenge
title_full The Higgs Machine Learning Challenge
title_fullStr The Higgs Machine Learning Challenge
title_full_unstemmed The Higgs Machine Learning Challenge
title_short The Higgs Machine Learning Challenge
title_sort higgs machine learning challenge
topic Particle Physics - Experiment
url https://dx.doi.org/10.1088/1742-6596/664/7/072015
http://cds.cern.ch/record/2016636
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